Clinical diagnostic criteria for premenstrual syndrome and guidelines for their quantification for research studies
Bibliographic record
Abstract
Premenstrual syndrome (PMS) encompasses a variety of symptoms appearing during the luteal phase of the menstrual cycle. Although PMS is widely recognized, the etiology remains unclear and it lacks definitive, universally accepted diagnostic criteria. To address these issues an international multidisciplinary group of experts evaluated the current definitions and diagnostic criteria of PMS and premenstrual dysphoric disorder (PMDD). Following extensive correspondence, a consensus meeting was held with the aim of producing updated diagnostic criteria for PMS and guidelines for clinical and research applications. This report presents the conclusions and recommendations of the group. It is hoped that the criteria proposed by the group will become widely accepted and eventually be incorporated into the next edition of the World Health Organization's International Classification of Diseases (ICD-11). It is also hoped that the proposed guidelines for quantification of criteria will be used by clinicians and investigators to facilitate diagnostic uniformity in the field as well as adequate treatment modalities when warranted.
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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 | 0.055 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.024 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".