Gender Differences in Premenstrual Syndrome and Premenstrual Dysphoric Disorder Diagnosis and Treatment among Japanese Obstetricians and Gynecologists: A Cross-Sectional Study
Bibliographic record
Abstract
Premenstrual symptoms are characterized by unpleasant psychophysical symptoms that appear during the luteal phase before menstruation and interfere with a woman's quality of life. Premenstrual syndrome (PMS) is a pathological condition with premenstrual symptoms, of which premenstrual dysphoric disorder (PMDD) is a particularly severe psychological symptom. This study aimed to examine the gender differences in the diagnosis and treatment of PMS and PMDD among obstetricians and gynecologists (OB/GYNs) in Japan. Data were obtained from the survey conducted by the Japanese Society of Obstetrics and Gynecology. We used data from 1,257 of the 1,265 OB/GYNs who are engaged in PMS/PMDD practice and reported their gender. Multivariate regression analysis adjusted for propensity scores was performed. Female OB/GYNs were more frequently engaged in treating patients with PMS/PMDD than males [odds ratio (OR) 1.74; 95% confidence interval (CI) 1.36-2.21]. With regard to the diagnostic methods, more female OB/GYNs selected the two-cycle symptom diary than males (OR 2.88; 95% CI 1.80-4.60). Regarding treatment, fewer female OB/GYNs selected selective serotonin reuptake inhibitors as their first-line drug (OR 0.39; 95% CI 0.17-0.89). Gender differences were found in the selection of PMS/PMDD diagnosis and treatment methods among Japanese OB/GYNs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".