Treatments in women experiencing natural menopause: a cohort study from the USA, the UK and Germany
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: This study aimed to describe treatment patterns among naturally menopausal women from the USA, the UK and Germany. METHODS: Using health claims (the USA) and electronic health records (the UK and Germany), women aged 40-65 years with a first record of natural menopause (index date) from 2009 to 2022 were identified. Women with a history of bilateral oophorectomy, total hysterectomy, endocrine therapy for breast cancer or hormone/non-hormone therapy for menopausal symptoms were excluded. Treatments evaluated following the index date were hormone therapy, benzodiazepines, antidepressants, anticonvulsants and the antihypertensive clonidine. RESULTS: In total, 1,260,742 (the USA), 214,374 (the UK) and 124,542 (Germany) women were included, and treatments were recorded in 38.8%, 33.4% and 28.8%, respectively. Among these, the majority received one treatment class, mostly hormone therapy (44.2% for the USA, 41.1% for the UK, 92.6% for Germany), benzodiazepines (25.3% for the USA, 6.8% for the UK, 2.2% for Germany) and antidepressants (18.6% for the USA, 33.5% for the UK, 4.1% for Germany). Discontinuation rates at 6 months from starting initial treatment were 75.0-88.0% for hormone therapy, 65.0-85.0% for antidepressants and ≥98% for benzodiazepines. Treatment switches occurred in 25.4% (the USA), 21.8% (the UK) and 1.7% (Germany). CONCLUSIONS: Continuation rates with current treatments for women experiencing natural menopausal symptoms are low, indicating an unmet need for effective and acceptable therapies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it