Validating the Carolina Premenstrual Assessment Scoring System (C-PASS): A New Standard for PMDD Diagnosis in Research and Drug Development

Abstract

This article provides a comprehensive analysis of the Carolina Premenstrual Assessment Scoring System (C-PASS), a standardized protocol for diagnosing DSM-5 Premenstrual Dysphoric Disorder (PMDD). Tailored for researchers and drug development professionals, we examine C-PASS validation against expert clinical diagnosis, demonstrating 98% correct classification. The content explores its methodological framework for operationalizing DSM-5 criteria, troubleshooting common diagnostic inconsistencies, and comparative advantages over retrospective reporting. We detail practical implementation through worksheet, Excel, and SAS formats, alongside its critical role in creating homogeneous patient cohorts for reliable clinical trials and pathophysiology research.

The Diagnostic Challenge: Understanding the Need for C-PASS in PMDD Research

The Problem of Variable Diagnostic Practices in PMDD Research

Premenstrual Dysphoric Disorder (PMDD) affects approximately 3-8% of women of reproductive age, causing severe distress and functional impairment [1]. Despite its recognition in the DSM-5 and ICD-11, research into this condition has been hampered by inconsistent diagnostic practices across research settings. The absence of standardized, quantitative thresholds for interpreting daily symptom ratings has compromised the construct validity of the PMDD diagnosis, creating significant challenges for studies seeking to elucidate its underlying pathophysiology or develop effective treatments [2] [1].

The fundamental issue lies in the complex, multilevel nature of the DSM-5 PMDD diagnosis, which requires confirmation of multiple dimensions—symptom content, cyclical pattern, clinical severity, and chronicity—through prospective daily ratings over at least two menstrual cycles [1]. Without standardized operational definitions, different research laboratories have established varying thresholds for what constitutes "severe" symptoms or "minimal" postmenstrual clearance, leading to heterogeneous study populations and complicating the comparison of research findings across studies [1]. This article examines how the Carolina Premenstrual Assessment Scoring System (C-PASS) addresses these critical methodological challenges.

The C-PASS Solution: Standardizing PMDD Diagnosis

The Carolina Premenstrual Assessment Scoring System (C-PASS) was developed as a standardized scoring protocol to operationalize DSM-5 PMDD criteria using the Daily Record of Severity of Problems (DRSP), the most widely-used daily symptom scale [2] [1]. Available as a worksheet, Excel macro, SAS macro, and R package, C-PASS translates the qualitative DSM-5 criteria into quantitative thresholds for each diagnostic dimension [3].

The C-PASS framework addresses four critical diagnostic dimensions, systematically converting each DSM-5 criterion into measurable parameters as illustrated in the table below:

Table 1: C-PASS Operationalization of DSM-5 PMDD Criteria

Diagnostic Dimension	C-PASS Operationalization	DSM-5 Requirement
Content	≥1 core symptom + ≥5 total symptoms	Criterion A: A total of 5 symptoms (including at least one core symptom)
Cyclicity	30% decrease from premenstrual to postmenstrual week + no symptom >3 postmenses	"...present in the week before menses...improve within a few days after the onset of menses"
Clinical Significance	Absolute severity ≥4 + duration ≥2 days premenstrually	"symptoms are associated with clinically significant distress or interference..."
Chronicity	≥2 symptomatic months	"in the majority of menstrual cycles..."

The following diagram illustrates the structured workflow of the C-PASS diagnostic procedure:

Experimental Validation: Protocol and Outcomes

Validation Study Methodology

The validation of C-PASS followed a rigorous comparative design to evaluate its diagnostic accuracy against expert clinical judgment [2] [1]. The study implemented the following protocol:

• Participant Recruitment: 200 women were recruited based on retrospectively-reported premenstrual emotional symptoms.
• Symptom Monitoring: Participants completed the Daily Record of Severity of Problems (DRSP) for 2-4 consecutive menstrual cycles. The DRSP measures all 11 DSM-5 PMDD symptoms on a 6-point Likert scale (1="Not at all" to 6="Extreme").
• Comparative Diagnosis: Each case received dual independent assessments—one by expert clinicians using visual inspection of DRSP ratings, and one generated by the C-PASS algorithm.
• Statistical Analysis: Diagnostic agreement between clinical and C-PASS diagnoses was calculated to determine classification accuracy.

This methodology specifically addressed the well-established limitation of retrospective symptom reporting, which has been shown to be a poor predictor of prospective diagnosis [2].

Validation Results and Comparative Performance

The C-PASS validation demonstrated exceptional diagnostic accuracy when benchmarked against expert clinical judgment. The system achieved a 98% overall correct classification rate, indicating near-perfect agreement with clinician diagnoses [2] [1].

Table 2: C-PASS Validation Study Outcomes

Metric	Result	Interpretation
Agreement with Expert Diagnosis	98%	Near-perfect alignment
Correct Classification Rate	98%	Exceptional diagnostic accuracy
Retrospective vs Prospective Correlation	Poor	Confirms limitation of retrospective recall

The validation study further confirmed that retrospective reports of premenstrual symptom increases showed poor predictive value for prospective C-PASS diagnosis, highlighting the critical importance of daily monitoring and standardized scoring [2].

Implications for Research and Clinical Practice

Advancing Research Methodologies

The implementation of C-PASS addresses fundamental methodological challenges in PMDD research:

• Sample Homogeneity: By applying consistent diagnostic thresholds, C-PASS ensures that study populations represent a uniform clinical entity, reducing confounding variables in pathophysiological studies and clinical trials [2].
• Cross-Study Comparability: Standardized diagnosis enables meaningful comparisons across different research initiatives, facilitating meta-analyses and accelerating scientific progress [1].
• Phenotypic Precision: The system's ability to identify subthreshold cases (termed Menstrually-Related Mood Disorder, or MRMD) allows for investigation of the full spectrum of menstrually-related mood disturbances [1].

Integration with Emerging Research Technologies

The C-PASS framework aligns with contemporary research approaches, including:

• Digital Health Applications: The availability of C-PASS as an R package [3] enables integration with digital health platforms and electronic patient-reported outcome systems.
• Neuroimaging Correlations: Standardized diagnosis facilitates the investigation of neurobiological correlates of PMDD, with recent studies showing structural and functional brain changes associated with the disorder [4].
• Comorbidity Research: The precise phenotyping enabled by C-PASS supports investigation of PMDD comorbidities with other conditions, such as the recently demonstrated association with ADHD [5].

Table 3: Essential Reagents and Tools for PMDD Research

Research Tool	Function/Application	Key Features
C-PASS Algorithm	Standardized DSM-5 PMDD diagnosis	Available as worksheet, Excel macro, SAS macro, and R package [3]
Daily Record of Severity of Problems (DRSP)	Prospective daily symptom tracking	Measures all 11 DSM-5 PMDD symptoms; 6-point Likert scale [1]
Structured Clinical Interview for DSM-5	Differential diagnosis	Rules out other mood disorders that may exacerbate premenstrually [1]
Neuroimaging Protocols (fMRI, PET)	Investigation of neural correlates	Identifies structural and functional brain changes across menstrual cycle [4]

The following diagram illustrates the integrated relationship between these research components in a comprehensive PMDD research program:

The Carolina Premenstrual Assessment Scoring System represents a methodological advancement in PMDD research by addressing the critical problem of variable diagnostic practices. Through its standardized operationalization of DSM-5 criteria, C-PASS enables the formation of well-defined, homogeneous patient cohorts essential for valid pathophysiological investigation and treatment development [2] [1].

The high diagnostic accuracy (98%) demonstrated in validation studies, combined with its availability through multiple computational platforms, positions C-PASS as a foundational tool for future PMDD research [3]. As the field continues to evolve—exploring neurobiological mechanisms [4], comorbidity patterns [5], and new therapeutic approaches—the implementation of standardized diagnostic methodologies will remain paramount to ensuring scientific rigor and accelerating progress toward effective interventions for this debilitating disorder.

Premenstrual Dysphoric Disorder (PMDD) represents a significant diagnostic challenge in clinical and research settings. The DSM-5 establishes precise, multilevel criteria that necessitate careful differentiation from ordinary premenstrual symptoms and other mood disorders [6]. The complexity of these diagnostic requirements has driven the development of standardized assessment protocols, most notably the Carolina Premenstrual Assessment Scoring System (C-PASS), which provides a structured methodology for applying DSM-5 criteria to prospective daily symptom data [2]. This validation research has been critical for establishing PMDD as a distinct depressive disorder and creating homogeneous samples for pathophysiological studies and therapeutic development.

DSM-5 Diagnostic Criteria for PMDD: A Detailed Analysis

The DSM-5 establishes specific criteria for diagnosing PMDD that require precise symptom patterns, timing, and functional impact [7] [8].

Core Symptom Requirements

Table 1: DSM-5 PMDD Symptom Criteria

Criterion	Requirement	Specific Symptoms
Timing (Criterion A)	Symptoms must occur in final week before menses onset, improve within few days after menses onset, become minimal/absent in week postmenses [7]	Must be present in majority of menstrual cycles [7]
Essential Mood Symptoms (Criterion B)	At least 1 required	1. Marked affective lability (mood swings, feeling suddenly sad/tearful, increased sensitivity to rejection)2. Marked irritability/anger or increased interpersonal conflicts3. Markedly depressed mood, feelings of hopelessness, self-deprecating thoughts4. Marked anxiety, tension, feelings of being keyed up/on edge [7]
Additional Symptoms (Criterion C)	Enough to reach total of 5 symptoms when combined with Criterion B	1. Decreased interest in usual activities2. Subjective difficulty in concentration3. Lethargy, easy fatigability, marked lack of energy4. Marked change in appetite; overeating; specific food cravings5. Hypersomnia or insomnia6. Sense of being overwhelmed or out of control7. Physical symptoms (breast tenderness/swelling, joint/muscle pain, "bloating," weight gain) [7]

Diagnostic Validation Requirements

Beyond symptom presence, DSM-5 establishes critical validation criteria:

• Functional Impact (Criterion D): Symptoms must cause "clinically significant distress or interference with work, school, usual social activities, or relationships with others" [7] [8].
• Exclusion of Other Disorders (Criterion E): The disturbance cannot be merely an exacerbation of another psychiatric disorder (though it may co-occur with others) [7].
• Prospective Confirmation (Criterion F): Criterion A must be confirmed by prospective daily ratings during at least two symptomatic cycles (provisional diagnosis permitted prior to confirmation) [7].
• Medical Exclusion (Criterion G): Symptoms cannot be attributable to substance effects or other medical conditions [7].

The C-PASS Validation: Standardizing DSM-5 Application

The Carolina Premenstrual Assessment Scoring System (C-PASS) was developed specifically to address challenges in consistently applying DSM-5 PMDD criteria [2].

C-PASS Methodology and Experimental Protocol

Table 2: C-PASS Validation Study Methodology

Aspect	Implementation in C-PASS Validation
Study Population	200 women recruited for retrospectively reported premenstrual emotional symptoms [2]
Assessment Tool	Daily Record of Severity of Problems (DRSP) completed for 2-4 months [2]
Comparison Standard	Expert clinical diagnosis using DSM-5 criteria [2]
Analytical Approach	Development of standardized scoring system (worksheet, Excel macro, SAS macro) for DSM-5 diagnosis [2]
Diagnostic Dimensions	Symptoms, severity, cyclicity, and chronicity based on DSM-5 criteria [6]

Key Validation Findings

The C-PASS validation yielded critical insights for PMDD diagnosis:

• Diagnostic Accuracy: Agreement between C-PASS diagnosis and expert clinical diagnosis demonstrated excellent concordance, with overall correct classification estimated at 98% [2].
• Retrospective vs. Prospective Assessment: Retrospective reports of premenstrual symptom increases proved to be poor predictors of prospective C-PASS diagnosis, highlighting the essential nature of prospective monitoring [2].
• Subthreshold Identification: C-PASS demonstrated sensitivity in identifying subthreshold PMDD (menstrual-related mood disorder) where patients experience sufficient distress and impairment to warrant treatment but don't meet full DSM-5 criteria [6].

C-PASS Validation Workflow

Comparative Assessment Tools for PMDD Diagnosis

Multiple instruments have been developed for PMDD assessment, with varying methodologies and applications.

Table 3: PMDD Assessment Tools and Methodologies

Assessment Tool	Methodology	Key Features	Application Context
Daily Record of Severity of Problems (DRSP)	Prospective daily ratings across menstrual cycles	Based on DSM-IV/DSM-5 criteria; 24-item form; high internal consistency (0.8-0.9) [6]	Primary symptom tracking tool; used with C-PASS system [2]
Carolina Premenstrual Assessment Scoring System (C-PASS)	Standardized scoring of prospective DRSP data	Four diagnostic dimensions (symptoms, severity, cyclicity, chronicity); Excel/SAS macros available [6]	Research standardization; sensitive to subthreshold PMDD [6]
Structured Clinical Interview for DSM-IV-TR PMDD (SCID-PMDD)	Diagnostic interview schedule	Developed in 2013; structured clinical assessment [6]	Diagnostic confirmation in clinical settings
Visual Analog Scales	Symptom intensity tracking	Likert scales ranging from "not present" to "severe" [9]	Clinical symptom monitoring; >60 instruments available [9]

Diagnostic Challenges and Implementation Barriers

The complex multilevel requirements of DSM-5 PMDD criteria present significant clinical and research challenges.

Diagnostic Complexity and Differentiation

The implementation of PMDD diagnostic criteria requires careful differentiation from several overlapping conditions:

• Distinction from PMS: While PMS involves physical symptoms and mild mood changes, PMDD is characterized by "severe enough symptoms to interfere with the ability to function, comparable with other mental disorders" [10].
• Exclusion of Psychiatric Exacerbations: Diagnosis requires determining whether symptoms represent a separate condition rather than "merely an exacerbation of the symptoms of another disorder" [7].
• Cyclical Pattern Confirmation: The characteristic pattern of symptoms worsening premenstrually and improving post-menses must be prospectively documented [7] [10].

Clinical Implementation Barriers

Several significant barriers impede proper PMDD diagnosis:

• Diagnostic Delays: Many patients "saw up to 10 different providers before receiving a diagnosis" [10].
• Symptom Normalization: Patients often initially assume symptoms are "normal or typical for menstruating people" and delay seeking care [10].
• Clinical Skepticism: Many patients report having "symptoms dismissed or invalidated by physicians," with some clinicians expressing doubt that PMDD is "real" [10].
• Misdiagnosis: Over one-quarter of PMDD patients are initially misdiagnosed with other psychiatric disorders [10].

PMDD Diagnostic Decision Pathway

Research Reagent Solutions for PMDD Investigation

The following essential materials and methodologies represent critical components for rigorous PMDD research.

Table 4: Essential Research Reagents and Methodologies

Research Tool	Function/Application	Implementation Example
Prospective Daily Rating Instruments (DRSP)	Tracks symptom timing, severity, and cyclicity across menstrual phases	Primary outcome measure in C-PASS validation; 2-4 months of daily ratings [2]
Structured Clinical Interviews (SCID-PMDD)	Standardizes diagnostic assessment and differential diagnosis	Confirmation of PMDD diagnosis excluding other psychiatric disorders [6]
Hormonal Assay Systems	Measures fluctuations in estrogen, progesterone, and other relevant hormones	Investigates neuroendocrine mechanisms in PMDD pathophysiology [6]
Standardized Scoring Algorithms (C-PASS)	Provides objective, replicable diagnostic classification based on prospective data	Excel and SAS macros for automated DSM-5 criteria application [2]
Control Populations	Differentiates PMDD from normal menstrual cycle-related symptoms	Asymptomatic controls or women with PMS without functional impairment [6]

The DSM-5 diagnostic criteria for PMDD establish a complex, multilevel framework that demands rigorous prospective confirmation and careful differential diagnosis. The development and validation of the Carolina Premenstrual Assessment Scoring System (C-PASS) represents a significant advancement in standardizing the application of these criteria, achieving 98% diagnostic agreement with expert clinical assessment [2]. This methodological progress is essential for creating well-defined patient populations for ongoing research into the neurobiological underpinnings of PMDD and for developing targeted therapeutic interventions. The persistent challenges in clinical diagnosis, including normalization of symptoms and provider skepticism, highlight the continued need for improved diagnostic tools and educational resources to ensure appropriate identification and treatment of this debilitating condition.

Historical Limitations of Retrospective Symptom Reporting

The historical reliance on retrospective symptom reporting has presented a significant challenge in the accurate diagnosis and research of Premenstrual Dysphoric Disorder (PMDD). Retrospective reporting requires individuals to recall symptom severity and timing days or weeks after their occurrence, a method particularly vulnerable to recall bias in conditions characterized by cyclical symptom patterns [1]. Before the development of standardized prospective tools, PMDD diagnosis often depended on these retrospective accounts, which poorly predicted actual prospective symptom patterns [2]. The Carolina Premenstrual Assessment Scoring System (C-PASS) was developed specifically to address these limitations by providing a standardized, prospective diagnostic methodology based on the DSM-5 criteria [2] [11]. This article examines the historical limitations of retrospective reporting through the validation research of C-PASS, which demonstrated a 98% correct classification rate when using prospective daily ratings compared to unreliable retrospective recall [2] [1].

Comparative Analysis: Retrospective Recall vs. Prospective C-PASS Diagnosis

Quantitative Comparison of Diagnostic Approaches

Table 1: Direct comparison of retrospective recall versus prospective C-PASS diagnosis

Diagnostic Dimension	Retrospective Recall Approach	C-PASS Prospective Approach	Impact on Diagnostic Validity
Symptom Timing Accuracy	Relies on patient memory of previous cycles	Daily ratings during symptomatic and asymptomatic phases	Eliminates recall bias in establishing cyclicity [1]
Symptom Severity Assessment	Subjective averaging of remembered symptoms	Objective daily ratings (1-6 scale) on DRSP	Quantifies absolute severity and relative change [1]
Cyclicity Confirmation	Patient perception of pattern	Calculated 30% decrease from pre- to post-menstrual scores	Objectively confirms DSM-5 timing requirements [1]
Diagnostic Reliability	Poor agreement with prospective diagnosis	98% correct classification vs. expert clinician	Creates homogeneous research samples [2]
Exclusion of Other Disorders	Difficult to distinguish from exacerbations	Clear documentation of symptom-free follicular phase	Identifies PMDD versus premenstrual exacerbation [1]

Table 2: C-PASS validation study outcomes comparing diagnostic methods

Validation Metric	Retrospective Recall Performance	C-PASS Performance	Statistical Significance
Agreement with Expert Diagnosis	Not reported	98% overall correct classification	p < .001 [2]
Prediction of Prospective Diagnosis	Poor predictor	Gold standard	Significant improvement (p < .001) [2] [1]
Sample Size in Validation	N/A	200 women with reported symptoms	Comprehensive validation [1]
Cycle Requirements	Single assessment	2-4 menstrual cycles of daily ratings	Meets DSM-5 "majority of cycles" requirement [1]
Internal Consistency	Not applicable	High (0.8-0.9)	Reliable across cycles [6]

Key Experimental Findings on Retrospective Reporting Limitations

The C-PASS validation study fundamentally demonstrated that retrospective reports of premenstrual symptom increases showed poor predictive value for actual prospective diagnosis [2]. This finding has profound implications for both clinical practice and research methodology. In a sample of 200 women recruited for retrospectively reported premenstrual emotional symptoms, the C-PASS system achieved 98% agreement with expert clinical diagnosis when using prospective daily ratings, starkly highlighting the unreliability of retrospective recall alone [2] [1]. This discrepancy underscores why DSM-5 specifically requires prospective daily ratings for PMDD diagnosis confirmation [6].

The C-PASS Diagnostic Methodology: A Standardized Approach

Experimental Protocol and Workflow

The C-PASS methodology implements a structured, multi-stage diagnostic process that rigorously operationalizes DSM-5 criteria through analysis of prospective daily ratings [12] [1]. The system is available through multiple formats, including a worksheet, Excel macro, SAS macro, and an R package (cpass), ensuring accessibility for researchers and clinicians across technical environments [12] [11].

Table 3: Essential research reagents and solutions for C-PASS implementation

Research Tool	Function	Implementation Specifications
Daily Record of Severity of Problems (DRSP)	Prospective daily symptom tracking	6-point Likert scale (1-6) for all DSM-5 symptoms; tracks functional impairment [1]
C-PASS Algorithm	Applies DSM-5 diagnostic thresholds	Standardized scoring for content, cyclicity, severity, chronicity dimensions [1]
Menstrual Cycle Mapping	Documents phase timing	Tracks premenstrual (-7 to -1) and postmenstrual (4-10) days relative to menses onset [1]
Data Formatting Tools	Prepares raw data for analysis	as_cpass_data() function (R package) transforms long-format data for diagnosis [12]

C-PASS Diagnostic Workflow: The multi-dimensional analysis process

Diagnostic Dimensions and Thresholds

The C-PASS system operationalizes DSM-5 criteria through four distinct diagnostic dimensions, each with specific thresholds derived from prospective daily ratings [1]:

• Content Dimension: Requires at least five total symptoms including one core emotional symptom (mood swings, irritability, depression, or anxiety), mapping DRSP items to specific DSM-5 criteria [1]
• Cyclicity Dimension: Mandates a minimum 30% decrease in symptom scores from the premenstrual week (days -7 to -1) to the postmenstrual week (days 4-10), with absolute postmenstrual scores not exceeding 3 on any day during days 4-10 [1]
• Severity Dimension: Requires absolute premenstrual symptom ratings of at least 4 (on the 6-point DRSP scale) for at least two days during the premenstrual week, with documented functional impairment [1]
• Chronicity Dimension: Confirms presence of symptoms across multiple cycles, requiring at least two symptomatic months as mandated by DSM-5 for "majority of menstrual cycles" [1]

Research Implications and Clinical Applications

Impact on PMDD Research Validity

The implementation of C-PASS addresses critical methodological challenges in PMDD research by creating well-defined, homogeneous patient samples [2]. Historically, variable diagnostic practices compromised the construct validity of PMDD and hindered research into its underlying pathophysiology [1]. By standardizing the translation of DSM-5 criteria into operationalized thresholds, C-PASS enables more reliable comparison across studies and facilitates the clear characterization of biological mechanisms and treatment efficacy [2] [1].

The system also identifies subthreshold cases through the Menstrually-Related Mood Disorder (MRMD) classification, capturing women who experience clinically significant distress and impairment but do not meet full PMDD criteria [1] [6]. This nuanced diagnostic capability allows researchers to study the spectrum of premenstrual disorders and potentially identify different underlying mechanisms or treatment responses.

Integration with Current Diagnostic Standards

The C-PASS methodology aligns with and implements the diagnostic requirements of both major classification systems. DSM-5 specifically requires that PMDD "should be confirmed by prospective daily ratings during at least two symptomatic cycles" [6], while ICD-11 includes PMDD as an independent diagnostic entity (code GA34.41) under genitourinary system diseases, cross-listed with depressive disorders [6]. The C-PASS system provides the standardized methodology to fulfill these diagnostic requirements with high reliability, addressing the historical limitations that necessitated its development [2] [11].

The historical limitations of retrospective symptom reporting in PMDD diagnosis have been effectively addressed through the development and validation of the Carolina Premenstrual Assessment Scoring System. By replacing unreliable retrospective recall with standardized prospective assessment across multiple diagnostic dimensions, C-PASS has demonstrated 98% diagnostic accuracy compared to expert clinical judgment [2]. This methodological advancement represents a significant improvement in PMDD research validity, enabling the creation of homogeneous patient samples and facilitating more reliable investigation into the disorder's underlying pathophysiology and treatment [1]. The C-PASS system stands as a critical methodological tool that has transformed PMDD diagnosis from subjective recall to evidence-based assessment, providing researchers and clinicians with a standardized approach that directly addresses the historical limitations of retrospective reporting.

Premenstrual Dysphoric Disorder (PMDD) affects a significant portion of the population, yet variable diagnostic practices have historically compromised the construct validity of this disorder and obscured research efforts to understand its underlying pathophysiology. Despite the inclusion of PMDD in the DSM-5, the lack of a standardized diagnostic methodology threatened the clarity of biological and treatment studies. The Carolina Premenstrual Assessment Scoring System (C-PASS) was developed specifically to address this critical methodological gap in women's mental health research. This robust diagnostic system provides researchers with a standardized framework for applying DSM-5 PMDD criteria to daily symptom reports, enabling the creation of well-defined, homogeneous research cohorts essential for reproducible scientific inquiry.

The development of C-PASS represents a significant advancement in methodological rigor for psychiatric research. By translating complex, multilevel diagnostic criteria into a reliable scoring protocol, C-PASS helps ensure that different research studies are effectively investigating the same underlying condition—a fundamental requirement for advancing our understanding of PMDD's neurobiology and developing more effective treatments.

The Carolina Premenstrual Assessment Scoring System (C-PASS) is a standardized scoring system specifically designed to operationalize DSM-5 diagnostic criteria for Premenstrual Dysphoric Disorder using prospective daily symptom monitoring. Developed by Eisenlohr-Moul and colleagues and published in the American Journal of Psychiatry, this tool provides a companion protocol to the Daily Record of Severity of Problems (DRSP), a established instrument for tracking premenstrual symptoms over time [2] [11].

C-PASS is available in multiple formats to accommodate different research environments and technical capabilities. Researchers can access the system as a manual worksheet for hand-scoring, an Excel macro for semi-automated analysis, or a SAS macro for advanced statistical programming environments [11]. This flexibility ensures that the methodology can be implemented across various research settings without requiring specialized computational expertise, thereby broadening its potential impact on the field.

The system is designed to be used with two or more months of daily symptom ratings, aligning with the DSM-5 requirement for prospective confirmation of symptoms across multiple cycles. This extended assessment period is crucial for distinguishing PMDD from other mood disorders with premenstrual exacerbation, a key diagnostic challenge in both clinical and research settings [2].

Comparative Analysis: C-PASS Versus Alternative Diagnostic Approaches

Performance Comparison with Clinical Diagnosis

The validation study for C-PASS demonstrated exceptional diagnostic reliability when compared to expert clinical assessment. The system achieved an overall correct classification rate of 98% when benchmarked against diagnoses made by experienced clinicians, establishing it as a highly valid method for operationalizing DSM-5 PMDD criteria [2].

Table 1: Diagnostic Accuracy of C-PASS Compared to Expert Clinical Diagnosis

Validation Metric	C-PASS Performance	Reference Standard
Overall Correct Classification	98%	Expert Clinical Diagnosis [2]
Agreement with Clinical Experts	Excellent	Clinical Interview [2]
Required Symptom Tracking Duration	2-4 months	Daily Record of Severity of Problems (DRSP) [2]

Advantages Over Traditional Diagnostic Methods

C-PASS addresses significant limitations of previously available diagnostic approaches. Perhaps most notably, the validation research demonstrated that retrospective reports of premenstrual symptom increases—a common initial screening method in both clinical and research settings—proved to be a poor predictor of prospectively confirmed PMDD diagnosis using the C-PASS system [2]. This finding highlights the crucial importance of prospective daily monitoring for accurate PMDD identification in research populations.

Table 2: C-PASS Compared to Alternative Diagnostic Approaches

Diagnostic Method	Key Features	Advantages	Limitations
C-PASS	Standardized scoring of 2+ months of daily ratings [2]	98% agreement with expert diagnosis; Consistent application of DSM-5 criteria [2]	Requires multiple cycles of data collection
Clinical Interview	Unstructured clinician assessment	Clinical expertise and judgment	Variable application of criteria; Lower reliability
Retrospective Recall	Single-timepoint symptom recall	Quick and easy to administer	Poor predictor of prospective diagnosis [2]
Other Scoring Systems	Various methods for daily data	Varies by system	Lack of standardization; Limited validation

The exceptional performance of C-PASS stems from its systematic approach to operationalizing each component of the DSM-5 criteria. Unlike clinical judgment, which can vary between practitioners, C-PASS applies the same rigorous standards across all cases, ensuring that research participants meet consistent diagnostic thresholds. This standardization is particularly valuable for multi-site studies where diagnostic consistency is essential for valid results.

Experimental Protocols and Validation Methodology

Core Validation Study Design

The validation research for C-PASS employed a rigorous methodological design to establish its reliability and accuracy. The study recruited 200 women who initially reported retrospective complaints of premenstrual emotional symptoms—a sampling approach designed to mimic real-world research recruitment strategies [2]. Each participant completed comprehensive daily symptom monitoring using the Daily Record of Severity of Problems (DRSP) for a period of two to four menstrual cycles, providing the prospective data required for both C-PASS scoring and expert clinical diagnosis.

The critical validation step involved comparing C-PASS-generated diagnoses with those determined by expert clinicians applying DSM-5 criteria through traditional clinical assessment. This head-to-head comparison allowed researchers to quantify the agreement between the standardized system and gold-standard clinical expertise. The remarkably high concordance rate of 98% provides strong empirical support for the use of C-PASS in research settings where expert diagnostic clinicians may not be available [2].

C-PASS Validation Workflow: This diagram illustrates the systematic approach used to validate C-PASS against expert clinical diagnosis.

Key Methodological Considerations

Several methodological aspects of the C-PASS validation study deserve emphasis. First, the use of multiple cycles of daily ratings reflects the DSM-5 requirement for prospective confirmation, enhancing the ecological validity of the validation approach. Second, the recruitment of participants based on retrospective symptoms rather than confirmed PMDD created a diagnostically heterogeneous sample, providing a more challenging and realistic test of the system's discriminative validity. Finally, the comparison with expert clinician diagnosis rather than simpler screening instruments established C-PASS as a genuine alternative to comprehensive clinical assessment for research purposes.

The validation findings also underscore an important implication for research design: retrospective symptom reports should not be used as a proxy for confirmed PMDD diagnosis in research studies, as they demonstrate poor predictive value for prospectively confirmed cases. This suggests that studies relying solely on retrospective screening may be including substantial numbers of false-positive cases, potentially confounding research findings on PMDD's underlying mechanisms and treatment response.

Essential Research Toolkit for PMDD Diagnostic Studies

Implementing rigorous PMDD diagnostic methodology requires specific assessment tools and protocols. The following research reagents and instruments form the foundation for reliable PMDD research using the C-PASS framework:

Table 3: Essential Research Materials for PMDD Diagnostic Studies

Tool/Resource	Function in Research	Key Features
C-PASS Protocol	Standardized DSM-5 PMDD diagnosis from daily ratings	Available as worksheet, Excel macro, or SAS macro [11]
Daily Record of Severity of Problems (DRSP)	Prospective daily symptom tracking	Validated instrument for PMDD symptoms [2]
Training Materials	Researcher education on proper administration	Ensure consistent implementation across sites
Data Collection Framework	Systematic organization of multi-cycle data	Templates for tracking completion and compliance

The DRSP serves as the essential data collection instrument that feeds into the C-PASS scoring system, providing the raw symptom data needed for diagnostic determination. For multi-site studies or longitudinal research, additional resources such as standardized training protocols for research staff and data management systems become increasingly important to maintain methodological consistency throughout the study duration.

Implications for Research and Clinical Trials

The implementation of C-PASS has significant implications for advancing PMDD research and drug development. By providing a standardized diagnostic method, C-PASS enables the creation of more homogeneous research samples, reducing noise in biological studies and increasing the likelihood of detecting true treatment effects in clinical trials [2]. This methodological advancement is particularly crucial for studies investigating the neurobiological underpinnings of PMDD, where clear diagnostic boundaries are essential for identifying valid biomarkers.

The availability of C-PASS also facilitates multi-site collaboration in PMDD research by ensuring consistent application of diagnostic criteria across different research centers. This standardization is particularly valuable for large-scale genetic studies, neuroimaging research, and multi-site clinical trials where diagnostic consistency is paramount. Furthermore, the system's high reliability makes it suitable for use by trained research staff rather than requiring expert clinicians at every study site, potentially increasing the efficiency and reducing the costs of PMDD research.

For pharmaceutical development and clinical trials, the use of C-PASS can help ensure that study populations truly meet DSM-5 criteria for PMDD, potentially reducing sample size requirements by creating more diagnostically pure groups and enhancing the ability to detect true drug-placebo differences. As regulatory agencies place increasing emphasis on standardized diagnostic approaches in mental health trials, tools like C-PASS provide the methodological rigor needed for adequate trial design.

The Carolina Premenstrual Assessment Scoring System represents a significant methodological advancement in PMDD research. By providing a standardized, validated approach to operationalizing DSM-5 criteria, C-PASS addresses a critical need for diagnostic consistency in both basic and translational research. The system's excellent agreement with expert clinical diagnosis, combined with its accessibility in multiple formats, makes it a valuable resource for researchers investigating this disabling condition.

Wider adoption of C-PASS in research settings promises to enhance the reliability and comparability of findings across studies, potentially accelerating our understanding of PMDD's etiology and treatment. As with any diagnostic tool, appropriate training and adherence to the specified protocols are essential for maintaining its validity in research applications. Researchers interested in implementing C-PASS can access the materials and seek additional information through the Center for Women's Mood Disorders at the University of North Carolina [11].

Operationalizing Diagnosis: The C-PASS Framework and Implementation

The Carolina Premenstrual Assessment Scoring System (C-PASS) represents a significant methodological advancement in psychiatric and women's health research. Developed to standardize the complex diagnosis of DSM-5 Premenstrual Dysphoric Disorder (PMDD), this tool provides researchers and clinicians with a reproducible framework for participant identification and study validation [1] [2]. The C-PASS was specifically designed to address critical inconsistencies in diagnostic practices that have historically compromised the construct validity of PMDD and hindered research into its underlying pathophysiology [1]. By translating the DSM-5 criteria into operationalized dimensions with specific thresholds, the C-PASS enables the formation of more homogeneous research cohorts, thereby enhancing the reliability and interpretability of scientific findings across pharmacological, neurobiological, and clinical studies [1] [6].

The Four Diagnostic Dimensions of C-PASS

The C-PASS operationalizes the DSM-5 criteria for PMDD into four distinct diagnostic dimensions, each with specific measurement criteria derived from daily symptom ratings. The system requires prospective daily monitoring using the Daily Record of Severity of Problems (DRSP) across a minimum of two menstrual cycles to ensure diagnostic accuracy [1] [11]. The following table summarizes these core dimensions and their specific implementation within the C-PASS protocol.

Table 1: The Four Core Diagnostic Dimensions of the C-PASS

Diagnostic Dimension	Description	C-PASS Implementation & Thresholds
Content	The nature and number of symptoms required for diagnosis [1].	- At least one core emotional symptom (e.g., affective lability, irritability/anger, depressed mood, anxiety/tension) [1].- A total of five symptoms from the combined list of core and secondary symptoms (e.g., decreased interest, concentration difficulties, lethargy, appetite/sleep changes, physical symptoms) [1].
Cyclicity	The characteristic pattern of symptom onset in the premenstrual phase and offset post-menses [1].	- Relative Premenstrual Elevation: A 30% increase in symptom severity from the postmenstrual week (days 4-10 of the cycle) to the premenstrual week (days -7 to -1, where -1 is the day before menstrual onset) [1].- Absolute Postmenstrual Clearance: Symptoms must not exceed a score of 3 (on a 1-6 DRSP scale) on any day during the postmenstrual week (days 4-10) [1].
Clinical Significance	The severity and impact of symptoms, ensuring they cause substantial distress or functional impairment [1].	- Absolute Premenstrual Severity: At least five symptoms must reach a score of 4 or higher (on the 6-point DRSP scale) during the premenstrual phase [1].- Symptoms must be associated with clinically significant distress or interference with work, school, social activities, or relationships [1].
Chronicity	The persistence of symptoms over multiple menstrual cycles [1].	- The symptom pattern must be present for a minimum of two symptomatic cycles [1].- This aligns with the DSM-5 requirement that symptoms occur in "the majority of menstrual cycles" over the preceding year [1].

Visualizing the Diagnostic Workflow

The following diagram illustrates the logical sequence for applying the four C-PASS dimensions to prospective daily ratings to arrive at a PMDD diagnosis.

Experimental Validation Protocol

The validation of the C-PASS was conducted through a rigorous methodological study comparing its diagnostic outcomes with expert clinical judgment.

Methodology

• Participant Cohort: The study enrolled 200 women who were recruited based on retrospectively reported premenstrual emotional symptoms [1] [2].
• Symptom Monitoring: Each participant provided prospective, daily symptom ratings using the Daily Record of Severity of Problems (DRSP) for a duration of two to four consecutive menstrual cycles [1] [2].
• Diagnostic Comparison: Diagnoses generated by the C-PASS algorithm were compared against those made by an expert clinician, which served as the reference standard [1] [2].
• Analysis: The agreement between the C-PASS diagnosis and the expert clinical diagnosis was quantified to determine the tool's classification accuracy [2].

Key Validation Results

The C-PASS demonstrated exceptional performance in its ability to reliably identify PMDD cases when benchmarked against expert clinical judgment.

Table 2: Key Outcomes from the C-PASS Validation Study

Validation Metric	Result	Interpretation
Agreement with Expert Diagnosis	Excellent [2]	The C-PASS showed a very high level of concordance with diagnoses made by experienced clinicians.
Overall Correct Classification	98% [2]	The system correctly classified the PMDD status (either positive or negative) for 98% of the participants in the study.
Prediction of Retrospective Reports	Poor [1] [2]	Confirmed previous evidence that retrospective recall of premenstrual symptoms is an unreliable predictor of a prospective PMDD diagnosis.

The Researcher's Toolkit: Essential Materials for C-PASS Implementation

For research teams aiming to implement the C-PASS protocol, the following tools and resources are essential.

Table 3: Essential Research Reagents and Resources for C-PASS Implementation

Resource	Function/Description	Availability
Daily Record of Severity of Problems (DRSP)	A validated self-report scale for daily tracking of all 11 DSM-5 PMDD symptoms on a 6-point Likert scale [1].	Widely used and cited in PMDD research; items mapped directly to DSM-5 criteria [1].
C-PASS Algorithm (Excel/SAS Macro)	A standardized, computerized scoring system that automates the application of the four diagnostic dimensions to DRSP data [1] [11].	Freely available for download; no permission required for use [11].
Structured Clinical Interviews (e.g., SCID)	Used to rule out other psychiatric disorders (Criterion E), ensuring the symptoms are not merely an exacerbation of another condition [1].	Standard tools in psychiatric research.

The Carolina Premenstrual Assessment Scoring System deconstructs the diagnosis of Premenstrual Dysphoric Disorder into four measurable, operationalized dimensions: Content, Cyclicity, Clinical Significance, and Chronicity [1]. This framework provides drug development professionals and researchers with a highly reliable and valid tool (98% correct classification) for identifying research cohorts, a critical step for ensuring the internal validity of studies investigating the neurobiology, genetics, and treatment of PMDD [2]. By standardizing the complex, multi-level DSM-5 criteria into a consistent protocol, the C-PASS mitigates historical diagnostic variability and promises to enhance the clarity, reproducibility, and cumulative progress of research aimed at understanding and treating this debilitating disorder [1] [6].

Integration with Daily Record of Severity of Problems (DRSP)

The Carolina Premenstrual Assessment Scoring System (C-PASS) represents a significant methodological advancement in the diagnosis of Premenstrual Dysphoric Disorder (PMDD). Developed to operationalize and standardize the DSM-5 diagnostic criteria, C-PASS provides a structured framework for interpreting prospective daily symptom data collected via the Daily Record of Severity of Problems (DRSP) [2] [1]. Prior to its development, variability in diagnostic practices across different research and clinical settings compromised the construct validity of PMDD, creating challenges for both treatment development and pathophysiological research [1]. The integration of C-PASS with DRSP addresses this critical need for reliability, transforming the complex, multilevel DSM-5 criteria into a consistent, automated scoring protocol that ensures homogeneous sample selection for clinical studies and drug development trials [2].

Comparative Analysis: C-PASS Diagnostic Performance

The validation of C-PASS against expert clinical diagnosis demonstrates its robust utility as a diagnostic tool for PMDD clinical research.

Table 1: Diagnostic Performance of C-PASS Against Expert Clinical Diagnosis

Performance Metric	Result	Methodological Context
Overall Correct Classification	98% [2] [1]	Comparison of C-PASS diagnosis with consensus diagnosis by expert clinicians.
Agreement with Expert Diagnosis	Excellent [2] [1]	Based on 200 women providing 2-4 menstrual cycles of daily DRSP ratings [1].
Predictive Value of Retrospective Reports	Poor [2] [1]	Confirms the necessity of prospective daily ratings for valid diagnosis.

Table 2: Comparison of PMDD Assessment Tools in Clinical Research

Tool	Primary Function	Key Features	Evidence of Validity
C-PASS	Standardized scoring for DSM-5 PMDD diagnosis [2] [13]	- Uses prospective DRSP data- Available as worksheet, Excel, SAS, and R macros [2] [3]	98% diagnostic accuracy vs. expert clinicians [2]
DRSP	Prospective daily symptom rating [1]	- Tracks all 11 DSM-5 PMDD symptoms- 6-point Likert scale (1-6) [1]	Established reliability and validity [1]
MAC-PMSS	Monitoring concurrent premenstrual and mood symptoms [14]	- Combines mood charting (NIMH-LCM) and premenstrual symptoms (DSM-5 based) [14]	Correlates strongly with DRSP (p<0.001) [14]

Experimental Protocols and Methodologies

Core C-PASS Validation Study Design

The seminal validation study for C-PASS employed a rigorous methodological protocol to ensure diagnostic accuracy [2] [1]:

• Participant Recruitment: 200 women aged 18-45 were recruited based on retrospectively reported premenstrual emotional symptoms [1].
• Prospective Symptom Tracking: Participants completed the Daily Record of Severity of Problems (DRSP) for two to four consecutive menstrual cycles. The DRSP measures all 11 DSM-5 PMDD symptoms on a 6-point severity scale (1=Not at all to 6=Extreme) [1].
• Diagnostic Comparison: Diagnoses generated by the C-PASS algorithm were compared against gold-standard diagnoses made by expert clinicians based on visual inspection of the same DRSP data [2].
• Symptom Quantification: The C-PASS algorithm operationalizes four key DSM-5 diagnostic dimensions:
  • Content: Requires ≥1 core emotional symptom and ≥5 total symptoms [1].
  • Cyclicity: Requires a 30% increase in symptom severity from the postmenstrual week (days 4-10) to the premenstrual week (days -7 to -1), with absolute postmenstrual symptoms not exceeding a value of 3 [1].
  • Clinical Significance: Requires premenstrual symptoms to reach an absolute severity of ≥4 for at least 2 days [1].
  • Chronicity: Requires the symptom pattern to be present for at least two symptomatic cycles [1].

Application in Comorbid Populations

Research has utilized C-PASS to investigate premenstrual exacerbation in populations with comorbid conditions. One study examined 15 females with Borderline Personality Disorder (BPD), using C-PASS to evaluate cyclical symptom patterns. Participants provided daily symptoms across 35 days, with ovulation and cycle phase confirmed through urine luteinizing hormone and salivary progesterone assays [15]. The study found that while the majority exhibited clinically significant perimenstrual symptom exacerbation, no participant met full DSM-5 criteria for PMDD, demonstrating C-PASS's specificity in differentiating pure PMDD from premenstrual exacerbation of other disorders [15].

Diagram 1: C-PASS Validation Workflow

Neurobiological Pathways and Diagnostic Specificity

The pathophysiology of PMDD involves a complex interaction between ovarian hormones and central nervous system neurotransmitters. Emerging evidence suggests that women with PMDD have an inherent vulnerability to normal hormonal fluctuations, particularly involving progesterone and its neuroactive metabolite allopregnanolone, and their impact on the serotonergic system and fronto-limbic circuit that regulates emotions [16]. C-PASS helps establish a diagnostically pure population for researching these underlying mechanisms by reliably excluding women whose symptoms represent premenstrual exacerbation of other ongoing mood disorders [1].

Diagram 2: PMDD Neurobiological Pathway

Essential Research Reagent Solutions

Table 3: Key Research Materials and Tools for PMDD Diagnostic Studies

Research Tool	Function/Application	Implementation in C-PASS Research
Daily Record of Severity of Problems (DRSP)	Prospective daily rating of all 11 DSM-5 PMDD symptoms [1]	Primary source of symptom data for C-PASS analysis [2]
C-PASS Algorithm	Standardized scoring system for DSM-5 PMDD diagnosis [2]	Available as worksheet, Excel macro, SAS macro, and R package (cpass) [11] [3]
Structured Clinical Interviews (SCID-I/SCID-II)	Rule out other mood, anxiety, and personality disorders [1] [15]	Critical for establishing Criterion E (exclusion of symptom exacerbation) [1]
Hormonal Assays	Confirm ovulation and cycle phase (e.g., salivary progesterone, urine LH) [15]	Used in advanced research designs to correlate symptoms with hormonal changes [15]
McMaster Premenstrual and Mood Symptom Scale (MAC-PMSS)	Monitor concurrent premenstrual and mood symptoms [14]	Useful for studying PMDD comorbidity with bipolar and major depressive disorder [14]

The integration of C-PASS with DRSP provides the methodological rigor necessary for advancing PMDD research and therapeutic development. By standardizing the translation of DSM-5 criteria into objective, data-driven diagnoses, this system enables the recruitment of homogeneous patient cohorts essential for reliably detecting treatment effects in clinical trials and investigating the neurobiological underpinnings of PMDD [2]. The high diagnostic accuracy (98%) established in validation studies confirms C-PASS as a robust tool for identifying true PMDD cases, while its ability to differentiate pure PMDD from premenstrual exacerbation of other disorders helps resolve key complexities in comorbidity research [1] [15]. For pharmaceutical developers and clinical researchers, consistent application of C-PASS with DRSP promises to enhance the validity and reproducibility of studies aimed at characterizing and treating this debilitating disorder [2] [13].

The Carolina Premenstrual Assessment Scoring System (C-PASS) provides a standardized, reliable method for diagnosing DSM-5 Premenstrual Dysphoric Disorder (PMDD) [2]. This system translates complex diagnostic criteria into an operationalized protocol compatible with existing research practices, addressing significant challenges in variable diagnostic practices that compromise the construct validity of PMDD [1]. The C-PASS was specifically developed to standardize the interpretation of prospective daily symptom ratings from the Daily Record of Severity of Problems (DRSP), ensuring consistent application of DSM-5 criteria across research settings [1].

This guide objectively compares the three available C-PASS formats—worksheet, Excel macro, and SAS macro—detailing their technical implementation, performance characteristics, and practical applications in research environments.

Experimental Protocol & Validation Methodology

The validation study for C-PASS established its reliability against expert clinical diagnosis [2].

Participant Cohort

• Sample Size: 200 women recruited for retrospectively reported premenstrual emotional symptoms [2] [1]
• Symptom Tracking: 2-4 months of daily symptom ratings using the Daily Record of Severity of Problems (DRSP) [2] [1]

Diagnostic Comparison

• Reference Standard: Diagnosis by expert clinicians
• Test Method: Diagnosis using the C-PASS system
• Analysis Method: Comparison of agreement between expert clinical diagnosis and C-PASS diagnosis [2]

Key Outcome Measures

• Overall correct classification rate
• Diagnostic agreement statistics
• Sensitivity to sub-threshold PMDD (menstrually-related mood disorder, or MRMD) [6]

C-PASS Format Comparison & Performance Data

The following table summarizes the technical specifications and performance characteristics of the three C-PASS implementation formats.

Table 1: Technical Comparison of C-PASS Implementation Formats

Format	Technical Requirements	Implementation Process	Performance & Output	Suitable Research Contexts
Worksheet	Paper format; manual calculation tools	Researcher scores DRSP data by hand using standardized worksheet	Direct implementation of C-PASS algorithm; dependent on researcher accuracy	Field studies; low-resource settings; small sample sizes; training environments
Excel Macro	Microsoft Excel with macro capabilities	Automated scoring via custom Excel macro; input DRSP data directly	Rapid analysis of multiple cycles; standardized output; reduces calculation errors	Clinical settings; moderate-scale studies; researchers with basic technical skills
SAS Macro	SAS statistical software license	SAS macro processes DRSP data files in batch mode	High-volume processing; integration with statistical analysis; maximal reproducibility	Large-scale trials; academic research; longitudinal studies; automated workflows

Table 2: Performance Characteristics of C-PASS System

Validation Metric	Result	Significance
Agreement with Expert Diagnosis	Excellent [2]	Establishes criterion validity
Overall Correct Classification	98% [2] [1]	High diagnostic accuracy
Prediction of Retrospective Reports	Poor predictor of prospective diagnosis [2]	Highlights importance of prospective tracking
Sub-Threshold Detection	Sensitive to MRMD [6]	Identifies clinically significant cases below full PMDD threshold

C-PASS Diagnostic Workflow

The C-PASS system operationalizes the complex, multilevel DSM-5 PMDD diagnosis through a structured workflow that evaluates four key diagnostic dimensions across multiple assessment levels [1].

C-PASS Diagnostic Algorithm Flowchart illustrates the systematic multi-dimensional evaluation process. The system analyzes prospective daily ratings from the Daily Record of Severity of Problems (DRSP) across four essential diagnostic dimensions required by DSM-5 [1]. The Content Dimension requires at least five symptoms including one core emotional symptom; the Cyclicity Dimension demands a 30% decrease in symptoms from premenstrual to postmenstrual weeks; the Clinical Significance Dimension requires symptoms of sufficient severity (≥4 on 1-6 scale) and duration (≥2 days); and the Chronicity Dimension confirms presence across multiple cycles [1]. This structured approach enables reliable differentiation between PMDD, sub-threshold menstrually-related mood disorder (MRMD), and non-diagnostic cases.

Research Reagent Solutions

Table 3: Essential Materials for C-PASS Implementation

Research Reagent	Function in C-PASS Protocol	Implementation Notes
Daily Record of Severity of Problems (DRSP)	Primary data collection instrument for daily symptom ratings	Measures all 11 DSM-5 PMDD symptoms on 6-point scale; essential for all C-PASS formats [1]
Structured Clinical Interview (SCID-I/II)	Rules out other mood, anxiety, and personality disorders	Addresses Criterion E: "Not merely an exacerbation of another disorder" [1]
Menstrual Cycle Tracking Tool	Documents cycle phases for timing analysis	Critical for establishing luteal-phase specificity of symptoms [2]
C-PASS Algorithm	Standardized scoring system for DSM-5 PMDD	Available in three formats to accommodate different research environments and technical capabilities [2]

The C-PASS system represents a significant advancement in PMDD research methodology by addressing critical limitations in diagnostic reliability. The availability of three implementation formats—worksheet, Excel macro, and SAS macro—ensires broad accessibility across diverse research settings while maintaining diagnostic consistency. The 98% agreement with expert clinical diagnosis demonstrates the robustness of this standardized approach [2].

For drug development professionals, the C-PASS system enables more reliable patient stratification and outcome measurement in clinical trials. For researchers, it provides a validated tool for investigating PMDD pathophysiology by ensuring homogeneous participant samples. The multiple format options allow researchers to select the implementation method that best aligns with their technical resources and study requirements while maintaining the integrity of the diagnostic algorithm.

Within the realm of premenstrual disorders, precise case definitions are paramount for clinical diagnosis and research reproducibility. Two key terms—Premenstrual Dysphoric Disorder (PMDD) and Menstrually-Related Mood Disorder (MRMD)—are often central to this field, including studies validating instruments like the Carolina Premenstrual Assessment Scoring System (C-PASS). While they share a common temporal link to the menstrual cycle, they represent distinct diagnostic concepts. PMDD is a formal psychiatric diagnosis with strict, enumerated symptom criteria as defined by the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [17] [8]. In contrast, MRMD is often used in research contexts as a broader umbrella term that encompasses a constellation of clinically significant affective, behavioral, and physical symptoms occurring in the late luteal phase [18] [19]. Understanding the distinction between these entities is critical for interpreting research findings, particularly in studies investigating underlying pathophysiological mechanisms, where the specific participant phenotype is a key variable.

Comparative Diagnostic Criteria

The fundamental distinction between PMDD and the broader category of MRMD lies in the specificity and strictness of their diagnostic criteria. The following table summarizes the key differences based on standardized definitions.

Table 1: Diagnostic Comparison between PMDD and Core MRMD

Feature	Premenstrual Dysphoric Disorder (PMDD)	Core Menstrually-Related Mood Disorder (MRMD)
Diagnostic System	Formal diagnosis in DSM-5 [17] [8]	A research and clinical classification; broader than PMDD [18] [20]
Symptom Requirements	At least 5 symptoms total, with one being a core affective symptom (e.g., mood swings, irritability, depressed mood, anxiety) [7] [8]	Symptoms are not specified; they may be somatic and/or psychological. The number of symptoms is not specified [20].
Symptom Timing	In the majority of menstrual cycles, symptoms must be present in the final week before menses, improve within a few days after onset, and become minimal or absent in the week post-menses [7].	Symptoms must recur in the luteal phase and are absent after menstruation and before ovulation [20].
Functional Impairment	Required: Symptoms must cause clinically significant distress or interference with work, school, usual social activities, or relationships [7] [17].	Required: Symptoms must cause significant impairment [20].
Diagnostic Confirmation	Prospective daily ratings during at least two symptomatic cycles are required for confirmation [7] [21].	Prospective daily ratings during at least two cycles are required [18] [20].

A key conceptual model for understanding the relationship between these disorders is that PMDD can be considered a severe, specific subtype within the broader MRMD spectrum. Research cohorts classified as MRMD often include women who meet the full DSM-5 criteria for PMDD as well as those with significant, impairing premenstrual symptoms that may not fulfill the strict, enumerated symptom count of PMDD [18]. For instance, one study noted that while it recruited women with MRMD, 84% of that MRMD group actually met the stricter DSM-IV criteria for PMDD [18]. Furthermore, the MRMD umbrella includes variants such as premenstrual exacerbation of an underlying disorder and progestogen-induced PMD, which are distinct from the core PMDD diagnosis [20].

Epidemiological and Etiological Distinctions

The prevalence and proposed etiological mechanisms for PMDD and MRMD differ, reflecting their varying diagnostic scopes. Epidemiological studies estimate that approximately 1.3% to 5.8% of women meet the strict diagnostic criteria for PMDD [21] [8]. In contrast, the broader category of MRMD is estimated to affect a larger proportion of premenopausal women, with approximately 30% experiencing some form of MRMD [18] [22]. This indicates that while severe, defined PMDD is less common, a significant portion of the female population experiences clinically relevant premenstrual symptomatology.

The underlying etiology of both PMDD and MRMD is complex and not fully elucidated, but it is believed to involve an abnormal sensitivity to the normal hormonal fluctuations of the menstrual cycle, particularly changes in estrogen and progesterone [21]. Research has moved beyond simple hormone level differences, as women with these disorders do not typically have aberrant hormone concentrations, but rather a heightened sensitivity to them [21]. This dysregulation is thought to impact neurotransmitter systems, including serotonin, GABA, and dopamine [21]. Furthermore, a history of depression is a significant vulnerability factor, reported in 30–70% of women with MRMD [18] [22]. There is also a strong genetic component, with heritability of premenstrual symptoms estimated to be between 30% and 80% [8]. Recent evidence suggests that psychosocial history, particularly a history of childhood sexual abuse (CSA), may define a distinct pathophysiological phenotype within MRMD, associated with unique neuroendocrine and pain processing profiles [18] [19].

Key Research Methodologies and Protocols

A cornerstone in the objective diagnosis of both PMDD and MRMD is the use of prospective, daily symptom tracking, which is essential to avoid the recall bias inherent in retrospective reports [21]. The Daily Record of Severity of Problems (DRSP) is a well-validated tool commonly used for this purpose in research settings [18] [21] [19]. The typical diagnostic protocol involves:

• Participant Screening: Recruitment of women presenting with severe premenstrual symptoms and asymptomatic controls. A comprehensive medical and psychiatric history is obtained, often using structured interviews like the MINI to rule out current Axis I disorders [18] [19].
• Prospective Monitoring: Participants complete the DRSP daily for at least two to three consecutive menstrual cycles [18] [21]. The DRSP quantifies the severity of emotional, behavioral, and physical symptoms on a 6-point scale (1=absent to 6=extreme) [18].
• Data Analysis and Diagnosis:
  • For MRMD, criteria often include: (a) at least a 30% increase in emotional symptom severity from the follicular phase (days 4-10) to the seven days preceding menses; (b) a rating of emotional symptoms as moderate, severe, or extreme on at least two premenstrual days; and (c) remission of symptoms after menses onset, with a symptom-free period of at least six days in the follicular phase [18] [19].
  • For PMDD, diagnosis follows the DSM-5 algorithm applied to the prospective data, requiring at least five specific symptoms (including core mood symptoms) that meet severity and timing criteria [7] [17].

Table 2: Key Reagents and Tools for Premenstrual Disorder Research

Research Tool / Reagent	Primary Function in Research
Daily Record of Severity of Problems (DRSP)	Prospective daily rating of symptom severity and functional impairment to confirm cyclicity and diagnose PMDD/MRMD [18] [21].
MINI International Neuropsychiatric Interview (MINI)	Structured diagnostic interview to assess current and past Axis I psychiatric disorders for participant screening [18] [19].
Intravenous Propranolol	Non-selective beta-adrenergic receptor (β-AR) blocker used to investigate the role of the sympathetic nervous system in pain sensitivity in MRMD [23].
Isoproterenol Sensitivity Test	A test used to assess and quantify β-adrenergic receptor responsivity in study participants [23].

Physiological Biomarkers and Experimental Data

Research into the pathophysiological mechanisms of premenstrual disorders has revealed potential biomarkers that may help differentiate these conditions from other mood disorders. A key finding is a blunted sympathetic nervous system (SNS) reactivity to stress in women with MRMD. Studies show that compared to non-MRMD controls, women with MRMD exhibit significantly reduced heart rate (HR) and cardiac index (CI) reactivity to mental stress, a phenomenon that appears to be independent of a history of depression [18] [22]. This blunted myocardial reactivity was also found to be a predictor of greater premenstrual symptom severity [22]. This contrasts with the SNS hyperactivity often seen in current melancholic depression, suggesting a distinct physiological phenotype for MRMD [18].

Furthermore, investigations into pain mechanisms have shown that women with MRMD display greater beta-adrenergic receptor (β-AR) responsivity and heightened sensitivity to experimental pain during the luteal phase [23]. A double-blind, placebo-controlled, crossover study demonstrated that β-AR blockade with propranolol (a non-selective beta-blocker) differentially affected pain perception. It decreased the affective component of clinical pain in the MRMD group and reduced pain intensity and unpleasantness from experimental pain in the control group, suggesting a dysregulated adrenergic mechanism in MRMD-related pain [23]. The relationship between physiological stress reactivity and premenstrual disorders can be conceptualized as follows:

Diagram 1: Stress and Pain Pathways in MRMD

These findings underscore that the MRMD/PMDD phenotype is associated with measurable dysregulation in both the sympathetic nervous system and adrenergic receptor function, providing a physiological basis for symptoms that can be targeted in therapeutic development.

Comorbidity and Differential Diagnosis

Accurate differential diagnosis is critical, as premenstrual disorders can co-occur with or be exacerbated by other conditions. A key diagnostic rule is that for PMDD or core PMD, the disturbance must not be merely an exacerbation of another disorder, such as Major Depressive Disorder (MDD), Panic Disorder, or a personality disorder, although it can co-occur with them [7] [20] [17]. This is where prospective daily rating is indispensable, as it helps distinguish a pure premenstrual disorder (with symptom-free follicular phases) from a premenstrual exacerbation of a ongoing underlying condition [20] [21].

Important comorbidities and differentials include:

• Major Depressive Disorder (MDD): A history of MDD is the most common comorbidity [8]. However, the blunted stress reactivity in MRMD is a differentiating factor from the hyperactive stress axes often seen in melancholic depression [18] [22].
• Migraine with Aura (MA): Research has identified a significant association between MRMD and MA, with one study finding a much higher prevalence of MA in women with MRMD (11/88) compared to non-MRMD controls (0/86) [19]. This comorbidity appears to be further heightened by a history of childhood sexual abuse [19].
• Other Medical Conditions: Hypothyroidism, anemia, endometriosis, and other conditions can produce symptoms similar to PMDD/MRMD and should be ruled out during diagnosis [21].

Implications for Research and Drug Development

The clear differentiation between PMDD and the broader MRMD spectrum has profound implications for clinical trials and drug development. The strict, symptom-based definition of PMDD creates a homogenous patient population, which is ideal for proving the efficacy of a new compound targeting specific emotional and physical symptoms listed in the DSM-5 [7] [8]. In contrast, the MRMD classification, often defined by a significant percent increase in symptom severity from the follicular to luteal phase, may capture a more heterogeneous group [18] [19]. This group includes women with sub-threshold PMDD and those for whom physical symptoms (e.g., pain, bloating) are more prominent than core affective symptoms. Interventions targeting broader neuroendocrine or inflammatory pathways, such as beta-adrenergic blockers for pain [23], may find a more relevant patient population within this broader MRMD spectrum.

Therefore, the choice of case definition directly impacts trial outcomes. Using PMDD criteria may demonstrate efficacy for a drug targeting severe dysphoria, while using MRMD criteria might be better suited for a drug aimed at relieving a wider array of treatment-resistant physical symptoms and functional impairment. Furthermore, the identification of distinct physiological biomarkers, such as blunted SNS reactivity [22] or altered beta-adrenergic function [23], provides objective, quantifiable endpoints beyond subjective symptom scores, offering new avenues for validating therapeutic mechanisms of action in targeted populations.

Optimizing Research Protocols: Addressing Common C-PASS Implementation Challenges

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) provides the standard diagnostic language for mental health disorders, yet its criteria often contain inherent ambiguities in severity thresholds that challenge research reliability and diagnostic consistency. This review examines the Carolina Premenstrual Assessment Scoring System (C-PASS) as a rigorous methodological solution that operationalizes DSM-5's qualitative criteria into quantitative, standardized measures. Focusing on premenstrual dysphoric disorder (PMDD) as a case study, we analyze how C-PASS's structured approach to defining diagnostic thresholds enhances diagnostic accuracy, reduces inter-rater variability, and creates more homogeneous research cohorts. The validation and implementation of C-PASS offer a replicable framework for addressing similar threshold ambiguities across the DSM-5 spectrum, promising significant advancements in both clinical practice and therapeutic development.

The publication of DSM-5 in 2013 marked the first major revision of psychiatric diagnostic standards in nearly two decades, incorporating advancements in neuroscience and clinical research to refine diagnostic categories [24]. Despite these improvements, the translation of DSM-5's qualitative diagnostic criteria into reliable, operationalizable measures for clinical research remains challenging. The manual often employs subjective terminology such as "marked," "severe," and "clinically significant" without providing standardized thresholds for these severity indicators, leaving substantial room for interpretation across different practitioners and research settings [25].

This diagnostic ambiguity is particularly problematic in pharmacological trials and neurobiological research, where precisely defined cohorts are essential for detecting valid treatment effects and identifying biological mechanisms. Variable diagnostic practices compromise construct validity and threaten the clarity of research aimed at understanding disorder pathophysiology [2] [1]. The problem is especially pronounced for disorders like premenstrual dysphoric disorder (PMDD), which requires prospective daily monitoring and complex, multilevel assessment of symptom patterns across multiple dimensions including content, cyclicity, severity, and chronicity [1].

The Carolina Premenstrual Assessment Scoring System (C-PASS) was developed specifically to address these challenges by providing standardized, operationalized criteria for PMDD diagnosis according to DSM-5 standards [11]. This review examines C-PASS as a case study in resolving diagnostic ambiguity, focusing on its methodological rigor, validation evidence, and implications for resolving similar threshold issues across other DSM-5 disorders.

DSM-5 Threshold Ambiguities: The Case of Premenstrual Dysphoric Disorder

PMDD's inclusion in DSM-5 represented a significant step in recognizing the substantial burden of severe premenstrual symptoms affecting approximately 3-8% of menstruating women [1]. The DSM-5 diagnostic criteria for PMDD require:

• A minimum of five symptoms from specific categories, with at least one being a core emotional symptom
• Symptoms occurring in the final week before menses onset
• Improvement within a few days after menses onset
• Symptoms being "minimal or absent" in the week post-menses
• Significant functional impairment
• Confirmation in the majority of menstrual cycles

While these criteria provide a conceptual framework, they lack operational precision in several critical dimensions. The DSM-5 does not specify quantitative thresholds for what constitutes "severe" symptoms, what degree of symptom reduction qualifies as "minimal or absent," or how many cycles constitute "the majority" [1]. This ambiguity has led to substantial variability in diagnostic practices across research settings, with different laboratories establishing their own thresholds for these key diagnostic components [1].

Table 1: Key Diagnostic Ambiguities in DSM-5 PMDD Criteria

Diagnostic Dimension	DSM-5 Qualitative Description	Unspecified Threshold Elements
Symptom Content	≥5 symptoms including 1 core emotional symptom	Severity threshold for counting a symptom as "present"
Symptom Cyclicity	"Improve within a few days after menses onset"	Percentage decrease required to qualify as "improvement"
Postmenstrual Clearance	"Minimal or absent in the week postmenses"	Absolute severity threshold for "minimal" symptoms
Symptom Chronicity	"In the majority of menstrual cycles"	Minimum number of symptomatic cycles required for diagnosis
Functional Impairment	"Clinically significant distress or interference"	Quantitative measure of impairment severity

The C-PASS Solution: Operationalizing Diagnostic Thresholds

The Carolina Premenstrual Assessment Scoring System (C-PASS) was developed as a standardized scoring system to translate DSM-5's conceptual PMDD criteria into precise, operationalizable measures [2] [1]. This system utilizes prospective daily symptom ratings from the Daily Record of Severity of Problems (DRSP) across a minimum of two symptomatic cycles, applying explicit thresholds to each diagnostic dimension.

Diagnostic Dimensions and Threshold Specifications

C-PASS addresses DSM-5's ambiguities by establishing quantitative thresholds for four key diagnostic dimensions:

• Content Dimension: Requires at least one core emotional symptom and a total of five symptoms reaching a predetermined severity threshold during the premenstrual phase [1].

• Cyclicity Dimension: Implements a 30% increase in symptom severity from the postmenstrual week (days 4-10 of the cycle) to the premenstrual week (days -7 to -1, where -1 represents the day before menstrual onset) [1].

• Clinical Significance Dimension: Establishes an absolute severity threshold of 4 or more (on a 6-point Likert scale) for premenstrual symptoms, with symptoms present for at least two days in the premenstrual week [1].

• Chronicity Dimension: Requires meeting diagnostic criteria across at least two symptomatic cycles, operationalizing the DSM-5's "majority of cycles" requirement for research purposes [1].

Table 2: C-PASS Operationalization of DSM-5 PMDD Criteria

Diagnostic Dimension	C-PASS Operational Definition	Measurement Threshold
Content Requirements	Number and type of symptoms	≥1 core symptom + ≥5 total symptoms at severity threshold
Relative Cyclicity	Premenstrual symptom elevation	≥30% increase from postmenstrual to premenstrual week
Absolute Postmenstrual Clearance	Postmenstrual symptom severity	No symptom >3 on 1-6 scale during postmenstrual week (days 4-10)
Absolute Premenstrual Severity	Premenstrual symptom intensity	Symptom severity ≥4 on 1-6 scale for at least 2 days premenstrually
Chronicity	Number of symptomatic cycles	Criteria met in ≥2 cycles

Implementation and Scoring

C-PASS is implemented through multiple platforms—including a worksheet, Excel macro, and SAS macro—to enhance accessibility across different research settings [11]. The system utilizes the Daily Record of Severity of Problems (DRSP), which measures all 11 DSM-5 PMDD symptoms with daily ratings on a 6-point scale (1="Not at all" to 6="Extreme") [1]. This standardized approach ensures consistent application of diagnostic thresholds across different research sites and clinical studies.

Figure 1: C-PASS Diagnostic Workflow - This diagram illustrates the sequential evaluation process for diagnosing PMDD using the Carolina Premenstrual Assessment Scoring System, showing how multiple diagnostic dimensions are systematically assessed.

Comparative Validation: C-PASS vs. Traditional Diagnostic Methods

Diagnostic Accuracy and Reliability

In the initial validation study involving 200 women with retrospectively reported premenstrual emotional symptoms, C-PASS demonstrated exceptional diagnostic agreement with expert clinical diagnosis, achieving a 98% overall correct classification rate [2] [1]. This high level of agreement is particularly noteworthy given the complexity of PMDD diagnosis, which requires simultaneous evaluation of multiple symptom domains across different temporal phases.

The validation study also confirmed that retrospective symptom reports—commonly used in clinical practice—were poor predictors of prospective C-PASS diagnosis, highlighting the critical importance of prospective daily monitoring for accurate PMDD diagnosis [1]. This finding underscores the limitations of diagnostic approaches that rely solely on patient recall rather than systematic, prospective data collection.

Impact on Research Cohort Homogeneity

By standardizing the application of DSM-5 criteria, C-PASS addresses the significant problem of heterogeneous research samples that has plagued previous PMDD studies. Variable diagnostic practices across research sites have compromised the construct validity of PMDD and obscured pathophysiological mechanisms [2]. The implementation of C-PASS produces more clearly defined, homogeneous samples of women with PMDD, thereby enhancing the internal validity of studies seeking to characterize and treat the disorder's underlying pathophysiology [1].

Table 3: Comparison of Diagnostic Approaches for PMDD

Diagnostic Characteristic	Traditional Clinical Diagnosis	C-PASS Standardized Diagnosis
Symptom Assessment	Retrospective recall or visual inspection of charts	Prospective daily ratings using validated DRSP
Symptom Severity Threshold	Subjective clinician judgment	Explicit threshold (≥4 on 6-point scale)
Cyclicity Requirement	Qualitative assessment of pattern	Quantitative (≥30% increase premenstrually)
Postmenstrual Clearance	Clinical impression	Explicit threshold (no symptom >3 postmenses)
Diagnostic Reliability	Variable between clinicians	High (98% agreement with expert diagnosis)
Research Utility	Heterogeneous samples	Homogeneous, well-characterized cohorts

Research Applications and Implementation Toolkit

Essential Research Materials and Protocols

The successful implementation of C-PASS in research settings requires specific materials and methodological approaches. The following toolkit outlines essential components for studies utilizing this diagnostic system:

Table 4: Research Reagent Solutions for C-PASS Implementation

Research Tool	Function/Purpose	Implementation Specifications
Daily Record of Severity of Problems (DRSP)	Prospective measurement of all 11 DSM-5 PMDD symptoms	6-point Likert scale (1-6); administered daily across 2-4 menstrual cycles
C-PASS Algorithm	Standardized application of diagnostic thresholds	Available as worksheet, Excel macro, and SAS macro
Menstrual Cycle Tracking	Accurate determination of menstrual phase	Calendar-based tracking of menses onset and duration
Structured Clinical Interviews	Exclusion of other psychiatric disorders	SCID-I for mood/anxiety disorders; SCID-II for personality disorders
Functional Impairment Measures	Assessment of symptom impact on daily functioning	WHODAS 2.0 or disorder-specific impairment scales

Experimental Protocol for Diagnostic Validation

The validation of C-PASS followed a rigorous methodological protocol that can serve as a template for similar diagnostic standardization efforts:

Figure 2: C-PASS Validation Study Design - This diagram outlines the methodological workflow for validating the Carolina Premenstrual Assessment Scoring System against expert clinical diagnosis, demonstrating the rigorous approach to establishing diagnostic accuracy.

• Participant Selection: Recruitment of women aged 18-45 reporting significant premenstrual symptoms, with exclusion criteria for conditions that might mimic or confound PMDD diagnosis [1].

• Prospective Monitoring: Collection of daily symptom ratings using the DRSP across a minimum of two complete menstrual cycles to capture symptom patterns across different phases [2].

• Blinded Diagnostic Comparison: Independent diagnosis by expert clinicians using traditional methods compared against C-PASS algorithm results, with diagnosticians blinded to each other's assessments [1].

• Statistical Analysis: Calculation of diagnostic agreement rates, sensitivity, specificity, and positive predictive value of C-PASS compared to clinical expert diagnosis [2].

Implications for Psychiatric Research and Drug Development

The implementation of standardized diagnostic systems like C-PASS has far-reaching implications for psychiatric research and therapeutic development. By resolving threshold ambiguities in DSM-5 criteria, such systems address fundamental challenges in mental health research:

Enhancing Pharmacological Trial Precision

For drug development professionals, the diagnostic precision offered by C-PASS directly addresses the problem of heterogeneous treatment effects in psychiatric clinical trials. By ensuring research participants meet consistent diagnostic criteria, C-PASS reduces outcome variability and enhances the ability to detect true medication effects [1]. This is particularly crucial for PMDD drug trials, where previous diagnostic inconsistencies have complicated the interpretation of treatment efficacy across studies.

The system's ability to identify subthreshold cases (termed Menstrually-Related Mood Disorder or MRMD in the C-PASS framework) also enables researchers to define more specific inclusion criteria for clinical trials, potentially identifying populations most likely to respond to particular interventions [6].

Advancing Neurobiological Research

Standardized diagnostic approaches like C-PASS are essential for neurobiological and genetic studies seeking to identify the underlying pathophysiology of psychiatric disorders. By creating more homogeneous patient groups, these systems reduce noise in biological data and enhance the likelihood of detecting meaningful biomarkers [1]. Recent evidence suggesting that PMDD may have neurodevelopmental underpinnings affecting the fronto-limbic circuit highlights the importance of precise phenotyping for biological studies [6].

The Carolina Premenstrual Assessment Scoring System represents a significant advancement in addressing the inherent threshold ambiguities in DSM-5 diagnostic criteria. By operationalizing the qualitative descriptions of PMDD into quantitative, measurable thresholds across content, cyclicity, severity, and chronicity dimensions, C-PASS demonstrates a replicable methodology for enhancing diagnostic reliability in psychiatric research.

The 98% diagnostic agreement with expert clinicians, combined with the system's accessibility through multiple platforms, positions C-PASS as both a validated research tool and a model for similar standardization efforts across other DSM-5 disorders [2] [1]. The implementation of such standardized diagnostic approaches is essential for advancing our understanding of psychiatric disorder mechanisms and developing more effective, targeted treatments.

For researchers and drug development professionals, tools like C-PASS offer the methodological rigor necessary to create well-characterized, homogeneous patient cohorts, ultimately strengthening the validity of clinical trials and neurobiological studies. As the field moves toward increasingly dimensional and biomarker-based diagnostic approaches, the principles embodied by C-PASS—standardization, operationalization, and validation—provide an essential framework for the next generation of psychiatric research.

The reliable diagnosis of Premenstrual Dysphoric Disorder (PMDD) represents a significant challenge in clinical psychiatry and research settings. The Carolina Premenstrual Assessment Scoring System (C-PASS) was developed specifically to standardize the complex, multilevel diagnosis of DSM-5 PMDD, which requires prospective daily symptom monitoring across menstrual cycles [1]. Within this framework, determining the optimal tracking duration—the number of menstrual cycles required for accurate diagnosis—becomes paramount for both diagnostic validity and research integrity. Variable diagnostic practices across laboratories have historically compromised the construct validity of PMDD, threatening the clarity of efforts to understand and treat its underlying pathophysiology [1]. The C-PASS addresses this challenge by providing a standardized scoring system for making DSM-5 PMDD diagnoses using daily symptom ratings from the Daily Record of Severity of Problems (DRSP) [13].

This objective comparison examines the evidence-based cycle requirements established through C-PASS validation research, contrasting these protocols with alternative diagnostic approaches and presenting supporting experimental data. For researchers and drug development professionals, understanding these methodological nuances is essential for constructing valid study designs, interpreting findings accurately, and advancing therapeutic development for this debilitating condition that affects 3-8% of women [1].

Diagnostic Framework: DSM-5 Requirements and Operationalization

The Multidimensional Nature of PMDD Diagnosis

DSM-5 PMDD diagnosis requires satisfaction of multiple dimensions across different levels (symptoms, cycles, individuals), creating a complex diagnostic picture that necessitates prospective tracking [1]. The C-PASS operationalizes these DSM-5 dimensions into standardized thresholds compatible with the Daily Record of Severity of Problems (DRSP), which measures all 11 DSM-5 PMDD symptoms on a 6-point scale [1]. The table below outlines how C-PASS translates these diagnostic dimensions into measurable criteria.

Table: Diagnostic Dimensions of PMDD Operationalized in C-PASS

Diagnostic Dimension	C-PASS Operationalization	DSM-5 Requirement
Content	≥1 core emotional symptom + ≥5 total symptoms	Criterion B & C: Specific symptom requirements
Cyclicity	30% decrease from premenstrual week to postmenstrual week + symptoms ≤3 during postmenstrual days 4-10	"Present in the week before menses...improve within a few days after onset"
Clinical Significance	Symptoms rated ≥4 (on 1-6 scale) for ≥2 days premenstrually	"Clinically significant distress or interference"
Chronicity	Presence in ≥2 menstrual cycles	"In the majority of menstrual cycles"

The Daily Record of Severity of Problems (DRSP)

The DRSP serves as the foundational measurement tool for C-PASS implementation, containing items that map directly onto DSM-5 PMDD criteria [1]. Core emotional symptoms tracked include mood swings, sensitivity to rejection, irritability/anger, depressed mood, and anxiety/tension [1]. Additional symptoms measured encompass decreased interest, concentration difficulties, lethargy, appetite changes, sleep disturbances, feeling overwhelmed, and physical symptoms [1]. This comprehensive daily assessment enables the precise quantification of symptom patterns necessary for applying C-PASS diagnostic algorithms across multiple cycles.

Comparative Analysis of Tracking Methodologies

C-PASS Protocol: Standardized Multi-Cycle Tracking

The C-PASS validation research established a robust protocol based on 2-4 menstrual cycles of daily symptom ratings using the DRSP [1] [2]. In the foundational validation study, 200 women recruited for retrospectively reported premenstrual emotional symptoms provided 2-4 menstrual cycles of daily symptom ratings, demonstrating the feasibility of this approach in a research context [2]. The C-PASS system is available in multiple formats—including a worksheet, Excel macro, and SAS macro—to facilitate standardized implementation across different research settings [1] [2].

The diagnostic accuracy achieved through this multi-cycle approach is noteworthy. When compared to expert clinical diagnosis, the C-PASS demonstrated excellent agreement with an overall correct classification estimated at 98% [1] [2]. This high level of reliability underscores the validity of the C-PASS methodology for PMDD diagnosis in research contexts where precise phenotyping is essential for studying underlying pathophysiology or treatment efficacy.

Alternative Diagnostic Approaches

Table: Comparison of PMDD Diagnostic Tracking Methods

Method Characteristic	C-PASS Protocol	Retrospective Recall	Single-Cycle Assessment	Visual Inspection Method
Tracking Duration	2-4 cycles	None (single assessment)	1 cycle	Typically 2-3 cycles
Standardization Level	High (computerized scoring)	Low	Moderate	Variable by rater
Diagnostic Accuracy	98% vs. clinical diagnosis	Poor predictor	Not validated	Laboratory-dependent
Evidence Base	Strong validation data	Evidence against validity	Limited evidence	Variable reliability
Key Limitation	Participant burden	Poor prospective validity	Unknown reliability	Subject to diagnostician error

Retrospective reporting of premenstrual symptoms represents the most common alternative to prospective tracking but demonstrates significant limitations. C-PASS validation research confirmed that retrospective reports of premenstrual symptom increases were a poor predictor of prospective diagnosis [2]. This finding aligns with previous evidence questioning the validity of retrospective assessment and underscores the necessity of prospective daily monitoring for accurate PMDD diagnosis.

Single-cycle assessment represents a compromise approach that reduces participant burden but lacks validation evidence. The visual inspection method—where diagnosticians visually interpret daily symptom ratings—has been used in research settings but suffers from significant inter-rater variability and differences in how laboratories translate daily ratings into diagnostic decisions [1]. The complex, multilevel nature of PMDD diagnosis creates high risk of diagnostician error with visual inspection methods, motivating the development of standardized systems like C-PASS [1].

Experimental Protocols and Validation Methodology

C-PASS Validation Study Design

The validation study for C-PASS employed rigorous methodology to establish its diagnostic accuracy [1] [2]. Participants were women aged 18-45 who reported significant premenstrual symptoms, representing the target population for PMDD diagnosis [13]. The core protocol involved:

• Daily Symptom Tracking: Participants completed the DRSP daily throughout 2-4 consecutive menstrual cycles, rating all symptoms on a 6-point scale from 1 ("not at all") to 6 ("extreme") [1] [13].
• Diagnostic Comparison: C-PASS diagnoses generated from the prospective ratings were compared against expert clinical diagnoses to determine concordance [2].
• Statistical Analysis: Classification accuracy was calculated, with the C-PASS demonstrating 98% correct classification compared to clinical expert diagnosis [1] [2].

This experimental design allowed for direct evaluation of how cycle tracking duration impacts diagnostic reliability within a standardized framework.

C-PASS Diagnostic Workflow

The following diagram illustrates the standardized C-PASS diagnostic workflow that researchers utilize to determine PMDD status based on prospective daily ratings:

C-PASS Diagnostic Decision Workflow

This standardized workflow ensures consistent application of DSM-5 criteria across all diagnostic assessments, eliminating the subjectivity that plagues visual inspection methods and creating homogeneous research samples essential for valid pathophysiology studies [1].

Essential Research Reagent Solutions

Table: Key Materials and Methods for C-PASS Implementation

Research Tool	Function in PMDD Diagnosis	Implementation Considerations
Daily Record of Severity\nof Problems (DRSP)	Prospective daily measurement of all DSM-5 PMDD symptoms	6-point Likert scale (1-6); maps directly to DSM-5 criteria; available in multiple languages
C-PASS Algorithm	Standardized scoring system for DSM-5 PMDD diagnosis	Available as worksheet, Excel macro, and SAS macro; requires 2-4 cycles of DRSP data
Structured Clinical\nInterview (SCID-I/II)	Rules out differential diagnoses (mood disorders, BPD)	Essential for Criterion E: "Not merely an exacerbation of another disorder"
Cycle Tracking Tool	Documents menstrual cycle phases for timing analysis	Identifies premenstrual (-7 to -1) and postmenstrual (4-10) phases relative to onset

These essential research tools form the foundation for valid PMDD diagnosis in research settings. The DRSP provides the raw data on symptom presence and severity, while the C-PASS algorithm transforms this data into standardized diagnoses according to DSM-5 criteria [1] [13]. Supplementary tools like the SCID ensure proper differential diagnosis, and precise cycle tracking enables accurate phase-based symptom analysis.

The C-PASS validation research provides compelling evidence for the necessity of multi-cycle prospective tracking in PMDD diagnosis. The 2-4 cycle protocol establishes an optimal balance between diagnostic accuracy and practical feasibility in research settings [1] [2]. This standardized approach addresses critical limitations of alternative methods, particularly the poor predictive validity of retrospective recall and the unreliability of visual inspection approaches [1] [2].

For researchers and drug development professionals, adherence to evidence-based cycle requirements is not merely methodological preference but fundamental to research validity. Consistent application of the C-PASS protocol across studies would yield more clearly-defined, homogeneous samples of women with PMDD, directly enhancing the clarity of studies seeking to characterize or treat the disorder's underlying pathophysiology [1]. As PMDD research advances toward elucidating biological mechanisms and developing targeted treatments, precise phenotyping through standardized multi-cycle assessment becomes increasingly critical for generating reproducible, clinically meaningful findings.

In the field of clinical research, particularly for conditions defined by cyclical symptom patterns, the integrity of prospectively charted data is paramount. This is especially true for premenstrual dysphoric disorder (PMDD), where the diagnosis hinges on the accurate daily documentation of symptoms across menstrual cycles. The Carolina Premenstrual Assessment Scoring System (C-PASS) was developed to standardize the complex process of diagnosing DSM-5 PMDD, transforming raw, daily symptom charts into reliable, research-grade data. The validation of the C-PASS itself represents a critical case study in ensuring data integrity throughout the research lifecycle, from collection to processing and analysis [2]. This guide explores the experimental validation of C-PASS and its foundational role in upholding data integrity, objectively comparing its performance against alternative diagnostic methods.

Experimental Validation of the C-PASS Protocol

The development and validation of the C-PASS were undertaken to address significant variability and compromise in PMDD diagnostic practices. The core objective was to create a standardized scoring system that would ensure the accuracy, consistency, and reproducibility of a PMDD diagnosis from prospective daily ratings [2].

Key Experimental Methodology

The validation study for C-PASS was designed to test its reliability against the gold standard of expert clinical diagnosis.

• Participant Recruitment: The study enrolled 200 women who were recruited based on retrospectively reported premenstrual emotional symptoms [2].
• Prospective Data Collection: Participants then provided two to four months of daily symptom ratings using the Daily Record of Severity of Problems (DRSP), a established self-monitoring tool [2].
• Diagnostic Comparison: Diagnoses generated by the automated C-PASS algorithm (available as a worksheet, Excel macro, and SAS macro) were directly compared to those made by expert clinicians [2].
• Outcome Measurement: The primary metric was the agreement rate between the C-PASS diagnosis and the expert clinical diagnosis. The study also assessed the predictive power of retrospective reports versus the prospective C-PASS method [2].

Performance Comparison of Diagnostic Methods

The table below summarizes the quantitative findings from the C-PASS validation study, comparing its performance to both expert diagnosis and retrospective reporting.

Diagnostic Method	Key Features	Agreement with Expert Diagnosis	Limitations / Notes
C-PASS Scoring System	Standardized algorithm for 2+ months of DRSP data [2].	98% overall correct classification [2].	Standardizes complex DSM-5 criteria; minimizes human error and bias.
Expert Clinical Diagnosis	Considered the validation gold standard [2].	Gold Standard	Subject to clinician variability and expertise; less scalable.
Retrospective Self-Report	Single-timepoint recall of symptoms [2].	Poor predictor of prospective diagnosis [2].	Highly susceptible to recall bias and inaccurate reporting.

Data Integrity Framework in C-PASS Validation

The C-PASS validation research exemplifies core principles of research data integrity, which are essential for the robustness and reliability of scientific findings [26].

Accuracy and Completeness

The protocol mandates prospective daily charting over multiple cycles, capturing data in near real-time to avoid the inaccuracies of retrospective recall. This ensures the data accurately represents the observed symptom patterns [2] [26].

Reproducibility and Standardization

By providing a transparent, automated algorithm for scoring, the C-PASS ensures that the same raw data will yield the same diagnostic outcome, regardless of the researcher, thereby guaranteeing processing reproducibility [2] [26].

Interpretability and Understandability

The C-PASS is a companion to the well-defined DRSP, which uses clear variable definitions and scales. This provides the necessary context, making the data and its subsequent processing understandable to other researchers [26].

Essential Research Reagents and Materials

The following table details key components required for implementing the C-PASS methodology in a research setting.

Item / Tool	Function in C-PASS Research
Daily Record of Severity of Problems (DRSP)	The validated daily diary instrument for collecting prospective symptom data. It captures the core symptoms of PMDD as defined by DSM-5 [2].
C-PASS Algorithm (Excel/SAS Macro)	The standardized scoring system that processes raw DRSP data to apply DSM-5 diagnostic rules algorithmically, ensuring objective and consistent diagnosis [2].
Data Dictionary / Codebook	A document defining all variables, coding for categories (e.g., symptom scales), and collection rules. This is crucial for interpretability and avoiding errors during analysis [26].
Secure Data Repository	A system for storing raw, unaltered DRSP data and its processed versions. Keeping the raw data preserves the original record for verification and re-analysis [26].

Workflow for Prospective Symptom Charting and Analysis

The diagram below illustrates the integrated workflow for prospective data collection and analysis using the C-PASS, highlighting how data integrity is maintained at each stage.

The validation of the Carolina Premenstrual Assessment Scoring System (C-PASS) underscores a fundamental principle in clinical research: a reliable diagnosis is the foundation of valid science. By implementing a rigorous, standardized protocol for prospective symptom charting, the C-PASS directly addresses critical threats to data integrity, such as recall bias and subjective interpretation. Its demonstrated 98% agreement with expert diagnosis sets a benchmark for reliability. For researchers and drug development professionals, employing robust tools like the C-PASS is not merely a methodological choice but an ethical imperative. It ensures that research cohorts are accurately defined, which in turn enhances the clarity of pathophysiological studies and the objective assessment of therapeutic efficacy for complex, cyclical conditions like PMDD.

The integration of automated statistical analysis pipelines into biological research has become crucial for enhancing the rigor, reproducibility, and transparency of research findings [27]. This is particularly relevant in specialized clinical research domains such as the validation of the Carolina Premenstrual Assessment Scoring System (C-PASS), a standardized scoring system for diagnosing DSM-5 Premenstrual Dysphoric Disorder (PMDD) using daily symptom ratings from the Daily Record of Severity of Problems (DRSP) [2]. The C-PASS system, available as a worksheet, Excel macro, and SAS macro, demonstrated excellent diagnostic reliability with 98% correct classification compared to expert clinical diagnosis in validation studies [2].

Within this context, R packages for automated statistical analysis offer significant advantages by standardizing analytical workflows, reducing human bias, and ensuring appropriate statistical test selection based on data characteristics. This article objectively compares the performance of two such solutions: the recently developed STATom@ic package, designed specifically for omic datasets, and the established performance package, which provides comprehensive assessment tools for statistical models [28] [27].

Package Profiles and Design Philosophies

Feature	STATom@ic	performance Package
Primary Focus	Automated statistical analysis of omic datasets [27]	Assessment, comparison, and testing of statistical models [28]
Development Context	Bioinformatics, translational research [27]	General statistical modeling, model diagnostics
Core Automation	Automatically selects statistical tests based on data properties and assumption checks [27]	Automates model diagnostics and performance assessment [28]
Key Functionality	Two-group and multi-group comparisons; assumption testing (normality, variance homogeneity) [27]	Comprehensive model performance metrics and diagnostic tests [28]
Statistical Approach	Partition-based testing guided by Shapiro-Wilk and Levene tests [27]	Performance assessment for existing models

Experimental Performance Data

In a precision-recall analysis comparing STATom@ic with DESeq2 (Wald test) for the same gene set, STATom@ic demonstrated higher precision but lower recall [27]. The following table summarizes the quantitative findings from the confusion matrix analysis:

Performance Metric	STATom@ic vs. DESeq2 (Wald Test)
Analysis Type	Confusion matrix precision-recall analysis [27]
True Positives	Both p-values < 0.05 [27]
False Positives	p < 0.05 only in STATom@ic [27]
True Negatives	Both p-values > 0.05 [27]
False Negatives	p > 0.05 only in STATom@ic [27]
Precision	High [27]
Recall	Lower than DESeq2 [27]

Detailed Methodologies

STATom@ic Workflow Protocol

STATom@ic implements a structured workflow to automatically select and apply appropriate statistical tests based on data characteristics. The package handles each variable separately, sorting them into partitions for appropriate testing rather than applying a one-size-fits-all approach [27].

Two-Group Comparison Workflow

For two-group comparisons, STATom@ic requires data to meet three fundamental assumptions: independence, random sampling, and continuous data [27]. The specific workflow includes:

• Residual Calculation: A generalized linear model (GLM) calculates residuals, which are then used in subsequent normality testing [27].
• Normality Testing: The Shapiro-Wilk test assesses normality distribution of residuals, selected for its reliability with small sample sizes typical in omic datasets [27].
• Variance Homogeneity Testing: For normal data (p ≥ 0.05 in Shapiro-Wilk test), the Levene test assesses equality of group variances [27].
• Statistical Test Selection:
  • Normal data with equal variances: Unpaired Student's t-test [27]
  • Normal data with unequal variances: Welch's test [27]
  • Non-normal data: Wilcoxon Mann-Whitney test [27]

Multi-Group Comparison Workflow

For multi-group comparisons, STATom@ic extends its automated workflow to accommodate more complex experimental designs [27]:

• Experimental Design Specification: Users can select either one-way or two-way ANOVA based on their experimental design [27].
• Assumption Checking: Similar to the two-group workflow, the package automatically checks for normality and homogeneity of variances [27].
• Post-Hoc Analysis: For significant ANOVA results, the package implements appropriate post-hoc tests, such as:
  • Tukey's HSD for equal variances [27]
  • Games-Howell or Dunnett's T3 for unequal variances [27]

STATom@ic Automated Analysis Workflow

Performance Package Assessment Protocol

The performance package focuses on model diagnostics and performance assessment rather than automated statistical test selection. Its methodology includes:

• Comprehensive Model Diagnostics: Automated checking of model assumptions including normality of residuals, homogeneity of variances, and influential observations [28].
• Performance Metrics Calculation: Automated calculation of multiple model performance indices including information criteria (AIC, BIC), R-squared measures, and other goodness-of-fit statistics [28].
• Model Comparison: Facilitates comparison between multiple fitted models to identify the best performing model [28].

Application to C-PASS Validation Research

Research Reagent Solutions

Research Tool	Function in C-PASS/PMDD Research
Daily Record of Severity of Problems (DRSP)	Validated daily symptom rating scale used as primary data collection instrument [2]
C-PASS Scoring System	Standardized scoring system (worksheet, Excel macro, SAS macro) for DSM-5 PMDD diagnosis [2]
STATom@ic R Package	Automated statistical analysis of symptom patterns and treatment outcomes
performance R Package	Validation of statistical models used in diagnostic algorithm development
ggplot2 R Package	Data visualization for symptom patterns and research findings [29] [30]

Integrated Analytical Framework for PMDD Research

The validation of complex diagnostic systems like C-PASS requires a multi-stage analytical approach that integrates various statistical tools and packages:

PMDD Research Analytical Framework

Comparative Analysis and Recommendations

Analysis Aspect	STATom@ic	performance Package
Automation Level	High automation of test selection and execution [27]	Diagnostic automation for existing models [28]
Bias Reduction	Reduces bias via automated assumption testing [27]	Identifies model deficiencies [28]
Precision	High precision in omic data analysis [27]	N/A
Recall	Lower recall compared to DESeq2 [27]	N/A
Use Case in C-PASS	Analysis of daily symptom patterns, treatment effects	Validation of diagnostic models, algorithm performance

Implementation Considerations for Researchers

When implementing these automated solutions in PMDD research, several critical factors emerge:

• Complementary Package Usage: STATom@ic and performance serve complementary roles rather than competing functions. STATom@ic excels in initial data analysis of symptom patterns and biological measures, while performance provides essential model validation capabilities for diagnostic algorithms [28] [27].

• Data Quality Requirements: Both packages require proper data preparation to function optimally. STATom@ic specifically notes that data with identical values across all samples in multiple groups or missing values should be pre-processed before analysis [27].

• Visualization Integration: The analytical outputs from both packages can be effectively visualized using R's ggplot2 package, which offers extensive customization capabilities for creating publication-quality graphics [29] [30]. When creating visualizations, researchers should ensure sufficient color contrast and consider color blindness accessibility by using R's improved default color palettes available in version 4.0.0 and later [31].

• Methodological Transparency: Automated packages enhance research transparency by providing standardized analytical workflows, which is particularly important for PMDD research given the historical challenges in diagnostic reliability [2]. The C-PASS system itself represents an important step toward standardizing PMDD diagnosis, with which these automated statistical tools align methodologically [2].

Validation and Comparative Analysis: C-PASS Performance in Research Settings

The reliable diagnosis of complex psychiatric conditions represents a significant challenge in clinical research and drug development. Within this context, the Carolina Premenstrual Assessment Scoring System (C-PASS) emerges as a rigorously validated diagnostic instrument for Premenstrual Dysphoric Disorder (PMDD). The development of C-PASS addresses a critical methodological gap by providing a standardized, prospective approach for applying DSM-5 diagnostic criteria to daily symptom ratings [2]. This validation research demonstrates exceptional diagnostic accuracy, achieving 98% agreement with expert clinical diagnosis [2], thus establishing a new benchmark for reliable PMDD assessment. For researchers and pharmaceutical developers working in women's mental health, the C-PASS protocol provides the methodological rigor necessary to define homogeneous patient populations for clinical trials and pathophysiological studies, ultimately enhancing the validity and reproducibility of research findings in this specialized field.

C-PASS Validation: Methodology and Performance

Core Diagnostic Methodology

The C-PASS operationalizes the DSM-5 diagnostic criteria for PMDD through a structured analysis of daily symptom reports. The system is designed as a companion protocol to the Daily Record of Severity of Problems (DRSP), which requires a minimum of two months of daily symptom tracking [2]. The scoring algorithm translates the complex, multi-component DSM-5 criteria into a standardized scoring system available in multiple formats (worksheet, Excel macro, and SAS macro) to enhance accessibility for research use [11] [2].

Key methodological components include:

• Prospective Daily Monitoring: Patients complete the DRSP for a minimum of two to three menstrual cycles, capturing symptom severity across emotional, behavioral, and physical domains.
• Symptom Cyclicity Analysis: The algorithm identifies symptoms that demonstrate a characteristic premenstrual pattern, fulfilling the specific timing criteria essential for PMDD diagnosis.
• Functional Impairment Assessment: The system evaluates the impact of symptoms on work, school, social activities, and relationships, a core requirement of DSM-5 diagnosis.
• Exclusionary Condition Screening: The methodology incorporates assessment of symptom independence from other medical or psychiatric disorders.

Experimental Validation Protocol

The validation study for C-PASS recruited 200 women who retrospectively reported premenstrual emotional symptoms [2]. Participants provided prospective daily symptom ratings using the DRSP for two to four months. The key experimental comparison involved matching C-PASS generated diagnoses against those made by expert clinicians applying DSM-5 criteria through traditional assessment methods.

Table 1: C-PASS Validation Study Design

Aspect	Description
Sample Size	200 women with retrospectively reported premenstrual emotional symptoms [2]
Data Collection Period	2-4 months of daily symptom ratings on the DRSP [2]
Reference Standard	Expert clinical diagnosis based on DSM-5 criteria [2]
Analysis Method	Comparison of C-PASS diagnosis with expert clinician diagnosis [2]

Diagnostic Performance Outcomes

The validation study demonstrated exceptional diagnostic performance, with C-PASS achieving 98% overall correct classification when compared to expert clinical diagnosis [2]. This near-perfect agreement underscores the reliability of the standardized scoring system in replicating expert clinical judgment. Furthermore, the study provided critical evidence that retrospective reports of premenstrual symptom increases were poor predictors of prospective C-PASS diagnosis [2], highlighting the essential role of prospective daily monitoring in achieving diagnostic accuracy and validating the C-PASS methodology as superior to retrospective recall.

Comparative Analysis with Alternative Diagnostic Approaches

Cross-Domain Diagnostic Validation Paradigms

To contextualize the performance of C-PASS within broader diagnostic validation research, we examine several recently validated artificial intelligence (AI) and deep learning diagnostic platforms across medical specialties. While these systems address different clinical conditions, they share common methodological principles of rigorous validation against expert clinical judgment or reference standards.

Table 2: Comparative Diagnostic Platform Performance

Diagnostic Platform	Clinical Application	Sensitivity	Specificity	Overall Accuracy/Agreement	Validation Sample
C-PASS	PMDD Diagnosis	N/A	N/A	98% vs. Expert Clinical Diagnosis [2]	200 women (2-4 months daily ratings) [2]
Deep Learning Model	Lung Cancer Detection	N/A	N/A	96-98% (Model Accuracy); 100% vs. 79% (Pathologist Accuracy) [32]	934 images (557 cancerous, 377 healthy) [32]
AI Platform	Acute Appendicitis	92.2% (Real Dataset); 99.2% (vs. CT) [33]	97.2% (Real Dataset); 90.9% (vs. CT) [33]	AUC: 0.97 (Real Dataset); 0.92 (vs. CT: 0.76) [33]	2,579 patients (real dataset); 9,736 (synthetic dataset) [33]
PolyAMiner-Bulk	Alternative Polyadenylation	N/A	N/A	Accurately identifies more APA changes vs. other methods [34]	Multiple genomic datasets [34]

Methodological Distinctions and Commonalities

The comparative analysis reveals several important patterns in modern diagnostic validation:

Validation Against Expert Judgment: Both C-PASS and the lung cancer deep learning model utilized expert clinical judgment as the reference standard, with C-PASS achieving 98% agreement with experts [2] and the cancer detection model surpassing pathologist accuracy (100% vs. 79%) [32]. This shared validation approach strengthens the clinical relevance of both systems.

Prospective vs. Retrospective Assessment: A key finding from the C-PASS validation with broader methodological implications was the poor predictive value of retrospective symptom reports compared to prospective monitoring [2]. This evidence challenges diagnostic approaches relying on patient recall across multiple medical domains.

Technical Architecture Variations: While C-PASS employs a structured scoring algorithm based on explicit DSM-5 criteria, the AI platforms utilize deep learning architectures including convolutional neural networks (CNNs) for image analysis [32] and attention-based algorithms for genomic data [34]. Despite different technical implementations, all systems share the goal of standardizing and improving diagnostic accuracy.

Implementation Workflows and Signaling Pathways

C-PASS Diagnostic Workflow

The following diagram illustrates the structured pathway for reliable PMDD diagnosis using the Carolina Premenstrual Assessment Scoring System:

Deep Learning Diagnostic Pathway

For comparative purposes, this diagram outlines the generalized workflow of deep learning-based diagnostic platforms like those used in medical imaging:

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Research Reagent Solutions for Diagnostic Validation Studies

Reagent/Material	Function in Research	Example Implementation
Daily Record of Severity of Problems (DRSP)	Prospective daily tracking of emotional, behavioral, and physical symptoms across menstrual cycles [2]	Core input material for C-PASS analysis; enables standardized symptom quantification [2]
C-PASS Algorithm (Worksheet/Excel/SAS)	Standardized application of DSM-5 criteria to daily symptom ratings [11] [2]	Converts raw DRSP data into reliable PMDD diagnoses with 98% expert agreement [2]
Whole-Slide Images (WSI)	High-resolution digital pathology samples for computational analysis [32]	Input data for deep learning models in cancer diagnostics (256px RGB resized) [32]
Convolutional Neural Networks (CNNs)	Deep learning architecture for image feature extraction and classification [32]	Backbone of AI diagnostic platforms for medical image analysis [32]
Residual Blocks with Skip Connections	Advanced neural network components addressing vanishing gradient problems [32]	Enables training of deeper networks for complex feature detection in medical data [32]
Synthetic Data Augmentation	Generation of additional training samples to improve model generalization [33]	Created dataset of 9,736 cases to validate appendicitis AI platform [33]

Discussion: Implications for Clinical Research and Drug Development

The validation of diagnostic instruments achieving 98% agreement with expert clinical judgment represents a significant advancement in clinical science. The C-PASS system specifically addresses the historical challenges in PMDD research related to heterogeneous patient populations and variable diagnostic practices [2]. By providing a standardized, reliable method for applying DSM-5 criteria, C-PASS enhances the construct validity of PMDD as a diagnostic entity and facilitates more targeted research into its underlying pathophysiology.

For pharmaceutical developers and clinical researchers, the implementation of rigorously validated diagnostic tools like C-PASS enables the recruitment of more homogeneous patient cohorts for clinical trials, potentially reducing sample size requirements and increasing statistical power for detecting treatment effects. The demonstrated superiority of prospective daily monitoring over retrospective recall [2] further underscores the importance of methodological rigor in endpoint measurement for clinical trials in mood and menstrual cycle-related disorders.

The parallel developments in AI-based diagnostic platforms across other medical domains suggest a broader trend toward computational standardization of complex diagnostic decisions. While the technological approaches differ—from rule-based scoring systems like C-PASS to deep learning networks for image analysis—the shared objective remains the enhancement of diagnostic accuracy, reliability, and standardization across clinical and research settings.

The Carolina Premenstrual Assessment Scoring System establishes a new standard for PMDD diagnosis, with validation research demonstrating 98% agreement with expert clinical judgment [2]. This performance positions C-PASS as an essential methodological tool for researchers and drug developers seeking to elucidate the neurobiological mechanisms of PMDD and develop targeted therapeutics. The comparative analysis with other high-accuracy diagnostic platforms reveals consistent methodological themes, particularly the value of standardized assessment protocols and prospective data collection. As diagnostic validation research evolves, the integration of rigorous methodology with clinical expertise will continue to drive improvements in both psychiatric and medical diagnostic practices, ultimately enhancing the precision and effectiveness of clinical research and therapeutic development.

Accurate diagnosis of Premenstrual Dysphoric Disorder (PMDD) has historically been challenged by methodological limitations, primarily reliance on retrospective recall of symptoms. The Carolina Premenstrual Assessment Scoring System (C-PASS) represents a significant methodological advancement by establishing a standardized prospective framework for DSM-5 PMDD diagnosis using daily symptom ratings [2]. This systematic review synthesizes evidence from validation studies demonstrating the superiority of this prospective assessment approach over traditional retrospective reports across multiple validation metrics.

The diagnostic evolution of PMDD underscores the importance of methodological precision. Initially described as Late Luteal Phase Dysphoric Disorder in DSM-III-R Appendix, the condition required prospective confirmation for official recognition in DSM-5 [6]. This transition reflects growing recognition that retrospective recall introduces significant measurement error that compromises diagnostic accuracy and construct validity [35] [36].

Comparative Methodology: Prospective versus Retrospective Assessment

The C-PASS Prospective Framework

The Carolina Premenstrual Assessment Scoring System utilizes two or more months of daily symptom ratings from the Daily Record of Severity of Problems (DRSP) to standardize DSM-5 PMDD diagnosis across four dimensions: symptom presence, severity, cyclicity, and chronicity [2] [6]. This methodology operationalizes the DSM-5 requirement that symptoms be confirmed prospectively in most menstrual cycles over the preceding year.

The technical implementation includes:

• Standardized scoring protocols available as worksheet, Excel macro, and SAS macro
• Structured clinical interview components aligned with DSM-5 criteria
• Sensitivity to sub-threshold PMDD cases (menstrual-related mood disorders)
• Internal consistency ratings of 0.8-0.9 across assessment domains [6]

Limitations of Retrospective Assessment

Retrospective reports require individuals to characterize subjective well-being or emotions from past cycles, introducing multiple sources of error. Cognitive memory research demonstrates that memory reconstruction often produces significant distortions of prior events and experiences [36]. Retrospective reports are particularly vulnerable to:

• Current emotional state bias influencing recall
• Symptom belief expectations overriding actual experiences
• Telescoping effects compressing or expanding symptom timelines
• Cultural normalization of premenstrual symptoms affecting reporting [36] [6]

Validation studies using Finnish register data confirm that measurement errors in retrospective event histories substantially bias analytical outcomes in medical research [35].

Head-to-Head Validation: Experimental Evidence

Diagnostic Accuracy Comparison

A direct comparison study recruiting 200 women with retrospectively reported premenstrual emotional symptoms provides compelling evidence for prospective assessment superiority [2]. Participants completed both retrospective reports and prospective daily ratings (2-4 months), enabling direct methodology comparison.

Table 1: Diagnostic Agreement with Expert Clinical Diagnosis

Assessment Method	Overall Correct Classification	Agreement with Expert Diagnosis	Predictive Value of Retrospective Reports
C-PASS (Prospective)	98%	Excellent	N/A
Retrospective Reports	Not reported	Poor	Poor predictor of prospective diagnosis

The C-PASS system demonstrated exceptional classification accuracy (98% overall correct classification) when validated against expert clinician diagnosis [2]. Critically, retrospective reports of premenstrual symptom increases proved to be a poor predictor of prospective C-PASS diagnosis, indicating limited clinical utility of recall-based methods [2].

Measurement Error and Analytical Bias

Method-comparison studies highlight fundamental differences in error structure between prospective and retrospective methods. Retrospective reports exhibit greater systematic error (bias) due to cognitive reconstruction processes, while prospective tracking minimizes recall bias through contemporaneous recording [35] [37].

Table 2: Measurement Error Characteristics by Assessment Type

Error Type	Retrospective Reports	Prospective Daily Ratings
Systematic Error (Bias)	High (emotion-related beliefs influence recall)	Low (minimized through immediate recording)
Random Error	Moderate (variable recall accuracy)	Controlled through standardized instruments
Contextual Influence	High (current mood affects memory)	Low (standardized rating scales)
Data Completeness	Variable (dependent on memory)	High (structured daily entries)

Evidence from Finnish register validation studies indicates that measurement error bias from retrospective reports produces sizeable bias in estimated covariate effects, particularly for education and earnings-related variables in health studies [35].

Implications for Research and Clinical Practice

Impact on PMDD Prevalence Estimates

The methodological differences between assessment approaches substantially influence PMDD prevalence estimates. Studies using retrospective methods report widely variable prevalence rates from 4% to 80% for premenstrual symptoms and up to 10% for PMDD [6]. This variability reflects both genuine population differences and, critically, measurement inconsistency introduced by retrospective recall methods.

In contrast, the C-PASS framework establishes standardized diagnostic thresholds that improve prevalence estimation accuracy and enable valid cross-population comparisons. This precision is particularly important for resolving questions about cultural variation in PMDD presentation and prevalence [6].

Advancing Biological Validation Studies

Methodological precision in phenotypic characterization is prerequisite for valid biological research. The prospective confirmation of PMDD diagnosis through C-PASS enables more reliable investigation of neurobiological mechanisms, including:

• Fronto-limbic circuit dysfunction in emotion regulation
• Sensitivity to gonadal hormonal fluctuations
• Neurodevelopmental underpinnings (ADHD, adverse childhood experiences) [6]

Without prospective symptom confirmation, biological studies risk contaminating samples with women experiencing premenstrual exacerbation of underlying mood disorders rather than true PMDD, potentially obscuring valid biological signals [2] [6].

Research Implementation Protocols

C-PASS Experimental Workflow

The following diagnostic workflow illustrates the standardized implementation of C-PASS assessment:

Comparative Evidence Synthesis

The relationship between methodological approaches and diagnostic outcomes can be visualized as follows:

Essential Research Toolkit

Table 3: Key Materials and Instruments for PMDD Validation Research

Research Tool	Function/Purpose	Implementation Specifics
C-PASS Protocol	Standardized DSM-5 PMDD diagnosis	Available as worksheet, Excel macro, and SAS macro
Daily Record of Severity of Problems (DRSP)	Prospective symptom tracking	21 items rated daily; covers affective, somatic, cognitive domains
Structured Clinical Interview for PMDD	Diagnostic confirmation	Aligns with DSM-5 criteria; establishes symptom chronology
Hormonal Assay Methods	Biological validation	Estradiol, progesterone level tracking across menstrual cycle
Statistical Validation Packages	Method comparison analysis	Deming regression, Bland-Altman plots, correlation analysis

Evidence from C-PASS validation studies demonstrates clear methodological superiority of prospective daily assessment over retrospective reports for PMDD diagnosis. The 98% correct classification rate achieved by C-PASS, compared to the poor predictive value of retrospective recall, establishes prospective monitoring as the validation standard for menstrual-related mood disorders [2].

Future methodological development should focus on:

• Culturally validated adaptation of prospective tools for global populations
• Digital platform integration for improved compliance with daily ratings
• Short-form screening protocols that retain predictive accuracy
• Biological marker correlation with prospectively confirmed symptom patterns

The rigorous validation framework established by C-PASS represents a significant advancement in the field, providing researchers and clinicians with standardized methodology to overcome the historical limitations of retrospective recall and advance our understanding of PMDD's underlying pathophysiology [2] [6].

Within the specialized field of premenstrual dysphoric disorder (PMDD) research, the Carolina Premenstrual Assessment Scoring System (C-PASS) represents a significant advancement in diagnostic standardization. The C-PASS is a standardized scoring system developed to operationalize the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for PMDD using prospective daily symptom ratings [2]. This guide provides an objective comparison of the C-PASS protocol against traditional diagnostic methods, detailing its experimental validation and highlighting its role in promoting cross-laboratory consistency for researchers and drug development professionals.

Experimental Validation of C-PASS

Core Validation Study Design

The initial validation study for the C-PASS established its reliability against expert clinical diagnosis [2]. The methodology was structured as follows:

• Participant Cohort: 200 women recruited based on retrospectively reported premenstrual emotional symptoms.
• Symptom Tracking: Participants completed the Daily Record of Severity of Problems (DRSP) for two to four months, providing longitudinal data on symptom severity.
• Diagnostic Comparison: Diagnoses generated by the C-PASS algorithm were compared against those made by expert clinicians, which served as the gold standard.
• Outcome Measure: The primary metric was the correct classification rate of the C-PASS relative to clinical expert diagnosis.

Key Experimental Findings

The validation study demonstrated that the agreement between C-PASS diagnosis and expert clinical diagnosis was excellent, with an overall correct classification rate of 98% [2]. This indicates a high level of concordance between the standardized tool and expert judgment. Furthermore, the study reinforced existing evidence that retrospective self-reports of premenstrual symptoms are a poor predictor of a prospective PMDD diagnosis, underscoring the critical need for standardized, prospective measurement tools like the DRSP and C-PASS in research settings [2].

Comparative Analysis of C-PASS vs. Alternative Methods

The table below summarizes a comparative analysis of the C-PASS against common alternative diagnostic approaches, based on its validation research.

Table 1: Comparison of PMDD Diagnostic Methods in Research

Feature	C-PASS Protocol	Retrospective Interview Only	Unstructured Daily Ratings
Standardization	High; uses a defined algorithm for DSM-5 criteria [2]	Low; relies on subjective recall and clinician interpretation	Variable; no standardized scoring method
Data Foundation	Prospective, daily ratings over 2+ cycles (DRSP) [2]	Retrospective recall of symptoms	Prospective, but unstructured data collection
Diagnostic Reliability	98% agreement with expert clinical diagnosis [2]	Poor predictor of prospective diagnosis [2]	Unknown; not systematically validated
Inter-Rater Consistency	High; automated scoring eliminates rater bias [11]	Moderate to Low; susceptible to clinician bias	Low; vulnerable to interpretation differences
Suitability for Multi-Site Trials	Excellent; ensures uniform endpoint assessment	Poor; high risk of methodological drift	Poor; lack of consistency across sites
Key Advantage	Homogenizes research samples, improving construct validity [2]	Quick and inexpensive to administer	Captures daily symptom fluctuations

The C-PASS Workflow and Signaling Pathway

The diagnostic process for PMDD using the C-PASS can be visualized as a workflow that transforms raw symptom data into a validated DSM-5 diagnosis. This process ensures that all research subjects meet consistent diagnostic criteria, which is fundamental for the integrity of clinical trials and neurobiological studies.

Diagram 1: The C-PASS Diagnostic Workflow. This diagram illustrates the sequential process from patient recruitment to a finalized PMDD diagnosis, highlighting the critical role of the standardized C-PASS algorithm in applying DSM-5 criteria consistently.

The Scientist's Toolkit: Essential Reagents and Materials

Successful implementation of the C-PASS protocol in a research setting requires specific materials and tools. The following table details these essential components.

Table 2: Essential Research Reagent Solutions for C-PASS Implementation

Item Name	Function/Description	Critical Application in Research
Daily Record of Severity of Problems (DRSP)	A validated daily symptom rating form that tracks the core symptoms of PMDD [2].	Serves as the primary source of prospective data for the C-PASS algorithm. Its consistent use is fundamental to data quality.
C-PASS Algorithm (Worksheet, Excel Macro, SAS Macro)	The standardized scoring system that operationalizes DSM-5 criteria based on DRSP data [2] [11].	Automates diagnosis, ensuring objectivity and eliminating inter-rater variability across different research laboratories.
Data Collection Platform (e.g., Electronic Survey App, Database)	A system for reliable and secure collection of daily DRSP entries from study participants.	Ensures data integrity, minimizes missing entries, and facilitates efficient data transfer into the C-PASS scoring tool.
Statistical Analysis Software (e.g., R, SPSS, SAS)	Software used for advanced statistical analysis of outcomes beyond the primary diagnosis.	Allows researchers to analyze symptom severity trajectories, treatment effects, and comorbidity patterns in well-characterized cohorts.

The C-PASS protocol offers a robust, reliable, and standardized method for diagnosing PMDD in research contexts. Its primary comparative advantage lies in its ability to generate homogeneous patient samples by replacing subjective judgment with an algorithmic application of DSM-5 criteria [2]. For researchers and drug development professionals, adopting the C-PASS is critical for enhancing the construct validity of PMDD studies, ensuring that findings across different laboratories are based on a consistently defined population. This standardization is a prerequisite for generating reproducible results, validating new therapeutics, and elucidating the underlying pathophysiology of the disorder.

Application in Special Populations and Comorbid Conditions

The Carolina Premenstrual Assessment Scoring System (C-PASS) represents a significant advancement in the standardized diagnosis of premenstrual dysphoric disorder (PMDD), a condition affecting an estimated 3-8% of women [1]. This article examines the application and validation of C-PASS across diverse populations and comorbid conditions, providing a critical comparison with alternative diagnostic approaches. The complex, multilevel nature of DSM-5 PMDD diagnosis necessitates sophisticated tools like C-PASS to ensure reliability across varied patient populations, including those with comorbid psychiatric conditions, differing cultural backgrounds, and complex medical histories [1] [6]. We evaluate quantitative validation data, detailed experimental protocols, and implementation resources to guide researchers and drug development professionals in applying this diagnostic system to specialized populations.

C-PASS Diagnostic Methodology and Validation

Core Diagnostic Framework

The C-PASS operationalizes DSM-5 PMDD criteria into a standardized scoring system applied to prospective daily symptom ratings [2]. It translates the four key diagnostic dimensions—symptom content, cyclicity, clinical significance, and chronicity—into specific, measurable thresholds using the Daily Record of Severity of Problems (DRSP) instrument [1]. The system requires at least two months of daily symptom ratings, acknowledging that retrospective reports of premenstrual symptoms poorly predict prospective diagnosis [2] [1].

Table 1: C-PASS Diagnostic Dimensions and Operationalization

Diagnostic Dimension	DSM-5 Requirement	C-PASS Operationalization
Content	≥5 symptoms including ≥1 core emotional symptom [1]	Specific DRSP items mapped to DSM-5 symptoms [1]
Cyclicity	Symptoms "present in the week before menses...improve within a few days after onset" [1]	30% decrease from premenstrual week to postmenstrual week; postmenstrual scores ≤3 [1]
Clinical Significance	"Clinically significant distress or interference" [1]	Absolute premenstrual severity ≥4 (6-point scale); impairment items rated ≥4 [1]
Chronicity	"In the majority of menstrual cycles" [1]	Requirements met for ≥2 months [1]

Validation Study Design

The initial validation study involved 200 women recruited for retrospectively reported premenstrual emotional symptoms who provided 2-4 months of daily symptom ratings on the DRSP [2] [1]. Diagnoses made by the C-PASS algorithm were compared against those made by expert clinicians using the same prospective daily ratings.

Table 2: Key Validation Metrics for C-PASS

Validation Metric	Result	Significance
Agreement with Expert Diagnosis	Excellent [2]	Standardizes diagnostic process
Overall Correct Classification	98% [2]	High diagnostic accuracy
Prediction of Retrospective Reports	Poor [2]	Confirms need for prospective monitoring

The exceptional 98% correct classification rate demonstrates C-PASS's reliability in translating complex diagnostic criteria into consistent outcomes [2]. This validation is particularly relevant for special populations where diagnostic confusion may occur, such as women with comorbid mood disorders or those taking medications that might affect menstrual cycles.

Comparative Analysis with Alternative Diagnostic Approaches

Diagnostic Modalities Comparison

When evaluating PMDD diagnostic approaches, researchers must consider structured interviews, retrospective questionnaires, and prospective daily rating systems with varying standardization methods.

Table 3: PMDD Diagnostic Method Comparison

Method	Key Features	Advantages	Limitations
C-PASS	Standardized algorithm for DSM-5 using ≥2 months DRSP [2] [11]	High reliability (98% accuracy), open access, handles complex diagnostic logic [2]	Requires multiple cycles, technical implementation
Clinical Interview with Prospective Charting	Expert clinician interprets daily ratings [1]	Clinical judgment integration, flexible application	Subject to diagnostician error, low standardization [1]
Structured Clinical Interview (SCID-PMDD)	Diagnostic interview schedule [6]	Structured assessment format	Limited validation literature
Retrospective Questionnaires	Self-report of premenstrual symptoms [2]	Rapid administration, low burden	Poor predictive validity for PMDD diagnosis [2]

Application in Complex Populations

The C-PASS system demonstrates particular utility in populations where accurate differential diagnosis is challenging:

• Patients with Comorbid Mood Disorders: C-PASS's rigorous prospective assessment and requirement of postmenstrual symptom clearance (scores ≤3 during days 4-10 of cycle) helps distinguish PMDD from premenstrual exacerbation of ongoing disorders [1] [3].

• Cross-Cultural Populations: While most validation studies have been conducted in Western populations, recent evidence suggests comparable PMDD prevalence between high-income and low- and middle-income countries, highlighting the need for standardized tools like C-PASS that can be adapted across cultures [6].

• Special Clinical Settings: The standardized nature of C-PASS makes it particularly valuable in multisite research networks and health systems implementing population health approaches, similar to Kaiser Permanente's molecular marker harmonization initiatives [38].

Experimental Workflow

The following diagram illustrates the standard C-PASS implementation workflow for research studies:

Research Reagent Solutions

Table 4: Essential Research Materials for C-PASS Implementation

Resource	Function	Availability
DRSP (Daily Record of Severity of Problems)	Prospective daily rating of all 11 DSM-5 PMDD symptoms on 6-point scale [1]	Public domain
C-PASS Algorithm	Standardized scoring system applying DSM-5 criteria to DRSP data [2]	Worksheet, Excel macro, SAS macro [11]
C-PASS R Package	Implementation of C-PASS procedure for diagnosis of PMDD and MRMD [3]	GitHub repository (lasy/cpass) [3]
Menstrual Cycle Tracking	Determination of cycle phases for symptom alignment [1]	Custom implementation required
Structured Clinical Interview	Rule out other disorders (SCID-I, SCID-II) [1]	Standardized instruments

Future Directions in Special Populations

Research on C-PASS applications in special populations remains limited but critically needed. Future validation studies should focus on:

• Cultural Adaptation: Developing culturally validated assessment tools while maintaining diagnostic standardization [6].

• Comorbid Conditions: Refining differential diagnosis between pure PMDD and premenstrual exacerbation of other disorders using the C-PASS framework [6] [3].

• Integrated Healthcare Settings: Implementing C-PASS in large health systems with electronic health record integration, similar to molecular marker data harmonization initiatives [38].

• Digital Implementation: Leveraging digital tools and computational approaches to enhance C-PASS accessibility and implementation in diverse settings [39].

The C-PASS system represents a robust methodological advancement for PMDD research, particularly valuable for studies requiring diagnostic consistency across multiple sites or populations with complex comorbid conditions. Its standardized approach addresses longstanding challenges in PMDD research methodology while providing flexibility for implementation across various research contexts.

Conclusion

The validation of C-PASS represents a significant advancement in PMDD research methodology, providing a standardized, reliable system for patient identification that addresses longstanding diagnostic inconsistencies. By operationalizing complex DSM-5 criteria into measurable dimensions, C-PASS enables the creation of homogeneous research cohorts essential for valid clinical trials and pathophysiological studies. For drug development professionals, this tool offers critical precision in patient stratification, potentially accelerating therapeutic discovery. Future directions should focus on cross-cultural validation of assessment tools, integration with digital health platforms for scalable data collection, and application of C-PASS frameworks to investigate PMDD's neurobiological underpinnings and treatment outcomes. Widespread adoption promises to enhance methodological rigor across PMDD research, ultimately improving diagnostic accuracy and therapeutic development for this debilitating condition.

References

• 1. Toward the Reliable Diagnosis of DSM-5 Premenstrual ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5205545/]
• 2. Toward the Reliable Diagnosis of DSM-5 Premenstrual ... [https://pubmed.ncbi.nlm.nih.gov/27523500/]
• 3. The cpass R package - Laura Symul [https://lasy.github.io/cpass/]
• 4. Neuroimaging's Potential to Transform PMDD Diagnosis [https://www.samphireneuro.com/en-us/blog/will-neuroimaging-revolutionise-the-diagnosis-of-pmdd?srsltid=AfmBOoos_bh3ku6wGx8857uwKfuP54GwNodhxRm_IJ2VcWscaWGkM0KH]
• 5. Increased risk of provisional premenstrual dysphoric ... [https://www.cambridge.org/core/journals/the-british-journal-of-psychiatry/article/increased-risk-of-provisional-premenstrual-dysphoric-disorder-pmdd-among-females-with-attentiondeficit-hyperactivity-disorder-adhd-crosssectional-survey-study/CD1DC6B31D4B009AB04F580C1189BC86]
• 6. Diagnostic validity of premenstrual dysphoric disorder [https://www.frontiersin.org/journals/global-womens-health/articles/10.3389/fgwh.2023.1181583/full]
• 7. Table 1, Diagnostic Criteria for Premenstrual Dysphoric ... - NCBI [https://www.ncbi.nlm.nih.gov/books/NBK279045/table/premenstrual-syndrom.table1diag/]
• 8. Premenstrual Dysphoric Disorder (PMDD) [https://www.psychdb.com/mood/pmdd]
• 9. Premenstrual Dysphoric Disorder Clinical Presentation [https://emedicine.medscape.com/article/293257-clinical]
• 10. Don't Miss the Diagnosis of Premenstrual Dysphoric Disorder [https://www.aafp.org/pubs/afp/afp-community-blog/entry/dont-miss-the-diagnosis-of-premenstrual-dysphoric-disorder.html]
• 11. For Clinicians & Researchers - Center for Women's Mood ... [https://www.med.unc.edu/psych/wmd/resources/clinicians-researchers/]
• 12. Introduction to the `cpass` package - Laura Symul [https://lasy.github.io/cpass/articles/cpass_vignette.html]
• 13. Improving PMDD Diagnosis: Insights from C-PASS - Belle App [https://bellehealth.co/c-pass/]
• 14. A DSM-5-based tool to monitor concurrent mood and ... [https://bmcwomenshealth.biomedcentral.com/articles/10.1186/s12905-022-01678-1]
• 15. Perimenstrual exacerbation of symptoms in borderline ... [https://www.cambridge.org/core/journals/psychological-medicine/article/perimenstrual-exacerbation-of-symptoms-in-borderline-personality-disorder-evidence-from-multilevel-models-and-the-carolina-premenstrual-assessment-scoring-system/C3618015C8AFED6E6C1108A573869E3D]
• 16. Diagnostic validity of premenstrual dysphoric disorder: revisited [https://pmc.ncbi.nlm.nih.gov/articles/PMC10711063/]
• 17. Table 3.24, DSM-IV to DSM-5 Premenstrual Dysphoric ... - NCBI [https://www.ncbi.nlm.nih.gov/books/NBK519704/table/ch3.t24/]
• 18. Menstrual Mood Disorders are Associated with Blunted ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3951307/]
• 19. The Association of Migraine with Menstrually Related ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3787370/]
• 20. Table 2, Classification of premenstrual disorders (PMD) - NCBI [https://www.ncbi.nlm.nih.gov/books/NBK279045/table/premenstrual-syndrom.table2clas/]
• 21. Premenstrual Syndrome and Premenstrual Dysphoric ... [https://www.aafp.org/pubs/afp/issues/2016/0801/p236.html]
• 22. Menstrual mood disorders are associated with blunted ... [https://www.sciencedirect.com/science/article/abs/pii/S0022399913004005]
• 23. Beta-Adrenergic Receptor Mechanisms and Pain ... [https://www.sciencedirect.com/science/article/pii/S1526590013010766]
• 24. The DSM-5: Classification and criteria changes - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC3683251/]
• 25. DSM-5 [https://en.wikipedia.org/wiki/DSM-5]
• 26. Guidelines for Research Data Integrity (GRDI) [https://www.nature.com/articles/s41597-024-04312-x]
• 27. STATom@ic: R Package for Automated Statistical Analysis ... [https://www.mdpi.com/2571-905X/8/1/18]
• 28. performance: An R Package for Assessment, Comparison and ... [https://joss.theoj.org/papers/10.21105/joss.03139]
• 29. Plotting Matrimony: A Step-by-Step Guide to Quantitative Data ... [https://majanalytics.medium.com/plotting-matrimony-a-step-by-step-guide-to-quantitative-data-visualization-in-r-using-dutch-a930d3fe34c3]
• 30. Chapter 3 Data visualisation with R - GitHub Pages [https://jjmedinaariza.github.io/modelling_book/data-visualisation-with-r.html]
• 31. Coloring in R's Blind Spot - The R Journal [https://journal.r-project.org/articles/RJ-2023-071/]
• 32. Development and Validation of a Deep Learning Model for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11048051/]
• 33. Multicenter validation of an artificial intelligence (AI) [https://www.ovid.com/journals/surge/pdf/10.1016/j.surg.2024.05.007~multicenter-validation-of-an-artificial-intelligence]
• 34. PolyAMiner-Bulk is a deep learning-based algorithm that ... [https://pubmed.ncbi.nlm.nih.gov/38325383/]
• 35. Measurement errors in retrospective of event histories. reports [https://ojs.ub.uni-konstanz.de/srm/article/view/2372]
• 36. | SpringerLink Retrospective Reports [https://link.springer.com/rwe/10.1007/978-94-007-0753-5_2515]
• 37. Points of Care in Using Statistics in Method-Comparison ... [https://www.westgard.com/essays/method-validation/essay19.html]
• 38. Abstracts From the 2025 Health Care Systems Research ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12279304/]
• 39. Addressing mental health challenges in clinical practice [https://link.springer.com/article/10.1007/s00406-025-02064-2]