Kampo Prescriptions for Premenstrual Syndrome and Premenstrual Dysphoric Disorder: A Secondary Analysis of Nationwide Survey by JSOG Women’s Health Care Committee
Bibliographic record
Abstract
Premenstrual syndrome (PMS) and Premenstrual Dysphoric Disorder (PMDD) significantly impair women's quality of life. A survey of obstetricians and gynecologists showed that 19.5% of the doctors preferred Kampo medicine, including Tokishakuyakusan (TSS), Kamishoyosan (KSS), Keishibukuryogan (KBG), and Yokukansan (YKS), as the first-choice treatment for these conditions. We aimed to analyze the characteristics of each Kampo prescription. A secondary analysis was conducted on survey results from members of the Japan Society of Obstetrics and Gynecology collected from September to November 2021. Data from 1,259 respondents treating PMS/PMDD were analyzed. Our correspondence analysis plotted relationships among treatments, showing that Kampo prescriptions of TSS, KSS, KBG, and YKS were distributed similarly to oral contraceptives (OCPs), but different from selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), and gonadotropin-releasing hormone analogs. Among the Kampo formulas, prescription YKS was the closest to SSRIs/SNRIs. Logistic regression analysis revealed that shorter physician experience (< 10 years, ≥ 10 and < 20 years) was associated with selecting prescriptions for TSS and KBG, whereas being a private practitioner was linked to selecting prescriptions for KSS and YKS (clinic vs. hospital OR: 1.57, 1.77; clinic vs. university OR: 1.53, 1.71). The prescription of YKS was also associated with choosing SSRIs/SNRIs (OR: 1.81), chasteberry (OR: 6.24), and other medications or supplements (OR: 2.41). Kampo prescriptions were strongly correlated with OCPs. Prescription TSS and KBG were likely chosen by less experienced practitioners, whereas prescription YKS was used by those more familiar with PMS/PMDD treatments, including SSRIs/SNRIs.
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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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".