A DSM-5-based tool to monitor concurrent mood and premenstrual symptoms: the McMaster Premenstrual and Mood Symptom Scale (MAC-PMSS)
Bibliographic record
Abstract
BACKGROUND: Despite high co-morbidity between premenstrual dysphoric disorder and mood disorders, there is a gap of research-based tools to monitor concurrent premenstrual and mood symptoms. In this study, we developed a new DSM-5-based questionnaire to prospectively monitor concurrent premenstrual and mood symptoms. METHODS: Fifty-two females with bipolar or major depressive disorder, ages 16-45, were enrolled in the study. Participants completed two months of prospective symptom charting including the McMaster Premenstrual and Mood Symptom Scale (MAC-PMSS) and the Daily Record of Severity of Problems (DRSP). At the end of the prospective charting, participants also completed the Montgomery-Åsberg Depression Rating Scale (MADRS), Hamilton Depression Rating Scale (HDRS) and the Young Mania Rating Scale (YMRS). The MAC-PMSS was correlated with the DRSP, MADRS, HDRS and YMRS. RESULTS: All individual items of the MAC-PMSS correlated strongly with the individual DRSP scores (all p < 0.001). The mood section of the MAC-PMSS also significantly correlated with MADRS (r = 0.572; p < 0.01), HDRS (r = 0.555; p < 0.01) and YMRS scores (r = 0.456; p < 0.01). CONCLUSIONS: The MAC-PMSS is a reliable to tool to measure concurrent mood and premenstrual symptoms in women with mood disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".