Community Pharmacists' Therapeutic Recommendations for Heavy Flow, Androgen Excess, Fragility Fractures and Night Sweats in Menstruating Women
Bibliographic record
Abstract
Background: Physicians and menstruating women often ask pharmacists for recommendations about menstrual cycle–related problems. Progesterone and medroxyprogesterone may provide physiology-based treatment, but official indications in menstruating women in Canada are minimal. Objectives: To describe pharmacists' responses to vignette-based questions about the treatment of common clinical problems in menstruating women and review evidence-based therapies. Methods: A pharmacist interviewer administered an 11-item questionnaire to a random sample of community pharmacists. Questions were based on clinical vignettes in adolescent, pre- and perimenopausal women and related to heavy flow, polycystic ovary syndrome, premenopausal osteoporosis, perimenopausal night sweats and side effects/contraindications for estrogens and progesterone/progestins. Results: The participation rate was 58%, including equal numbers of male and female pharmacists. Seventy-two percent indicated that they would treat menorrhagia in an anemic 13-year-old with oral contraceptives — 21% would recommend ibuprofen and 86% iron. Half recommended that a 35-year-old smoker with heavy flow and acne stop oral contraceptives, but the other 50% recommended a switch to an oral contraceptive with cyproterone. For premenopausal osteoporosis, the majority recommended calcium and vitamin D, but 53% endorsed oral contraceptives — only 7% suggested cyclic medroxyprogesterone. For night sweats, the majority recommended progesterone/progestin in a regularly menstruating 42-year-old woman. Estrogens are contraindicated with past thrombosis and/or breast cancer family history, and they could cause nausea; 50% of pharmacists also attributed these adverse effects to progesterone/progestins. Conclusions: Community pharmacists vary widely in their treatment choices for common pre- and perimenopausal women's menstrual cycle–related problems. The evidence in support of most recommendations is minimal or lacking.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".