Treatments in women experiencing natural menopause: a cohort study from the USA, the UK and Germany
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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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".