Patterns of use of tamoxifen and aromatase inhibitors: A population-based observational study
Bibliographic record
Abstract
556 Background: Few data are available outside clinical trials on the use of aromatase inhibitors (AI) as adjuvant treatment for early breast cancer (BC). We therefore used a large population-based database to describe patterns of use of AI over time, in comparison with tamoxifen, as well as switches between these regimens. Methods: We identified 13,479 women treated for BC with tamoxifen, anastrazole, letrozole, or exemestane between 1998 and June 2008 in the UK General Practice Research Database (GPRD). Patients were followed from their first prescription for 5 years or until recurrence, death, switch to another treatment or occurrence of a major thromboembolic or uterine event. Results: Mean age at cohort entry was 62 years (SD=14.0) in the tamoxifen group (n=10,806) and 70.8 (SD = 12.4) in the AI group (n=2,673). Overall, in the first year of treatment 88.8% of patients had prescriptions covering more than 80% of the year (88.3% and 90.8% in tamoxifen and in AI group respectively). Table 1 describes prescriptions covering less than 80% of the days for each year of treatment in 3 subgroups. Among women started on AI therapy diagnosed with BC after 2006, 9.6% switched treatment. Half of them switched from one AI to another AI, the other half switched from AI to tamoxifen. Switches occurred within the first year of treatment in 76% of cases. Among women over 50 years of age who started a tamoxifen therapy after 2000, 31% of women switched to AI in the course of the study, of which 12% within the first year of treatment (11.1% and 13.7% for women diagnosed in 2000–2004 and after 2005 respectively). Conclusions: The real-life patterns of use of tamoxifen and AI therapy demonstrate high rates of adherence. However, the relatively high percentage of switchers among AI users is suggestive of an association with side-effects. [Table: see text] No significant financial relationships to disclose.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".