Treatment patterns in women with breast cancer and endocrine therapy-related menopausal symptoms: a cohort study from the United States, United Kingdom, and Germany
Bibliographic record
Abstract
OBJECTIVE: To describe treatment patterns for menopausal symptoms in women taking endocrine therapy for breast cancer treatment/prevention from the United States (US), United Kingdom (UK), and Germany. METHODS: We undertook a retrospective cohort study using data from the US Market Scan Commercial Claims and Encounters Data database, and electronic health records from the UK's Clinical Practice Research Database Aurum and the German Disease Analyzer. Women aged 18-65 years with a first prescription/dispensation for endocrine therapy for breast cancer treatment or prevention (index date) from 2010 to 2022 were followed up, and the following treatment classes were evaluated: antidepressants, benzodiazepines, anticonvulsants, antihypertensives, and hormone therapy. RESULTS: Treatments were recorded in 32.7% (39,137/119,717) US women, 20.4% (8,350/40,956) UK women, and 8.3% (1,031/12,388) German women. Among these, ~80% in the US and UK, and all in Germany, received one treatment class; switches occurred in 20.5% (US) and 16.5% (UK). The most frequent initial treatment classes were antidepressants (31.7% US, 45.3% UK, 38.1% Germany); the second most frequent were benzodiazepines (30.3% US), anticonvulsants (24.3% UK), and hormone therapy (27.2% Germany). Among antidepressants, the most common were venlafaxine (US and Germany), and amitriptyline, sertraline, and citalopram (UK). Six-month continuation rates for antidepressants were 42% (US), 12% (UK), and 7% (Germany); continuation rates for other treatments were even lower. CONCLUSIONS: Continuation rates with available treatments for menopausal symptoms in women receiving endocrine therapy for breast cancer treatment/prevention are very low. This indicates a clear unmet need for safe, effective, and well-tolerated treatments in this patient population.
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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 | 0.001 |
| 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".