Women’s treatment preferences for moderate-to-severe vasomotor symptoms associated with menopause: insights from the WARMER study, a discrete choice experiment
Bibliographic record
Abstract
Objective: Elicit preferences for treatment of vasomotor symptoms (VMS) associated with menopause. Design: Discrete choice experiment. Setting: Australia, Canada, Denmark, France, Germany, Spain, Sweden, United Kingdom. Population: Women aged 40–65 years, postmenopausal, self-reporting ≥14 moderate-to-severe VMS episodes/week. Methods: Targeted review of published literature, steering committee feedback, iterative qualitative interviews and available clinical data identified potentially relevant attributes of VMS treatments. Main Outcome Measures: Women made a series of choices between two hypothetical treatments and an opt out differing in moderate-to-severe VMS frequency, other menopause symptom improvement, time to symptom improvement, 5-year risks of breast cancer, blood clots and osteoporosis. Data analysed using a mixed-methods approach. Relative attribute importance (RAI) captured the maximum contribution of each attribute to treatment choice depending on expected duration of hormone therapy (HT). Results: The most influential attribute was 5-year blood clot risk (RAI 26.4–28.4%). Improving other menopause symptoms had a 1.4 times greater effect on preferences than reducing VMS frequency. Based on 5–9 years’ HT use (RR 1.97), breast cancer risk was the fourth most important attribute. Improvements in other menopause symptoms, VMS frequency reduction, onset time and of osteoporosis risk reduction were 1.0–1.4 times more important than remaining attributes. Women were willing to accept an extra 0.5% blood clot risk of or an extra 0.25% breast cancer risk for every 10% reduction in VMS frequency. Conclusions: Women valued safe efficacious VMS treatment, with high importance on avoiding long-term risks. Reducing VMS frequency mattered over and above wider menopause symptoms.
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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 | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".