The association between endocrine therapy use and osteoporotic fracture among post-menopausal women treated for early-stage breast cancer in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The use of endocrine therapy for early-stage breast cancer, particularly aromatase inhibitor therapy has been associated with an increased risk of osteoporosis and fracture in clinical trials. We sought to validate this observation in real-world practice. METHODS: We used health administrative data collected from post-menopausal women (aged ≥66 years) who were diagnosed with breast cancer and started on adjuvant endocrine therapy from 2005 to 2012. Patients were classified by use of either an aromatase inhibitor or tamoxifen and followed until 2017 for a new diagnosis of an osteoporotic fracture. A multivariable analysis using a Cox proportional hazards model was adjusting for age, medical co-morbidities, medication use and duration of endocrine therapy. RESULTS: We identified 12,077 patients of whom 73% were treated with an aromatase inhibitor as compared to 27% with tamoxifen. Our multivariable analysis did not demonstrate any significant difference in the rate of osteoporotic fracture between patients treated with an aromatase inhibitor when compared with tamoxifen [Hazard ratio (HR) = 1.09; 95% confidence interval (CI) = 0.96-1.23, p-value = 0.18]. The 5-year rate of osteoporotic fracture for patients treated with either an aromatase inhibitor or tamoxifen was 7.5% and 6.9%, respectively. A completed sensitivity analysis did observe a decreased risk of fracture associated with tamoxifen usage over time. CONCLUSION: We could not detect a significant difference in the rate of osteoporotic fracture among patients treated with an aromatase inhibitor versus tamoxifen. Nonetheless, the risk with tamoxifen was numerically lower and significantly decreased when accounting for total duration of endocrine therapy.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".