Use of tamoxifen and aromatase inhibitors in a large population-based cohort of women with breast cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Non-compliance with oral treatment in oncology is an emerging health issue. For breast cancer (BC) patients, few data are available on compliance and persistence to tamoxifen in younger women and to aromatase inhibitors (AIs) as compared with tamoxifen in older women. METHODS: We constituted a cohort of 13,479 women with BC who received at least one prescription of tamoxifen or AI between 1998 and 2008, in the United Kingdom General Practice Research Database. Days covered by medication and treatment discontinuation were studied. Time to treatment discontinuation was calculated using Kaplan-Meier estimates. RESULTS: Overall, 18.9% (95% CI: 15.1-23.0) of women on AIs as compared with 31.0% (95% CI: 29.6-32.2) of women on tamoxifen had discontinued their treatments within the first 5 years (P<0.001). This rate raised to 50.7% (95% CI: 43.0-57.9) among the 416 women under 40 years receiving tamoxifen as initial hormonal therapy. Among older women, treatment discontinuation was less frequent for AIs as compared with tamoxifen (P<0.001). Among women on AI therapy, 14% of them (n=374) had switched treatments. CONCLUSION: Among older women, the real-life patterns of use of AI show high rates of compliance. In younger women, tamoxifen is prematurely discontinued for half of patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it