Fracture Risk in Women with Breast Cancer Initiating Aromatase Inhibitor Therapy: A Registry-Based Cohort Study
Bibliographic record
Abstract
Abstract Background Aromatase inhibitors (AIs) used in breast cancer induce loss in bone mineral density (BMD) and are reported to increase fracture risk. Materials and Methods Using a population-based BMD registry, we identified women aged at least 40 years initiating AIs for breast cancer with at least 12 months of AI exposure (n = 1,775), women with breast cancer not receiving AIs (n = 1,016), and women from the general population (n = 34,205). Fracture outcomes were assessed to March 31, 2017 (mean, 6.2 years for AI users). Results At baseline, AI users had higher body mass index (BMI), higher BMD, lower osteoporosis prevalence, and fewer prior fractures than women from the general population or women with breast cancer without AI use (all p < .001). After adjusting for all covariates, AI users were not at significantly greater risk for major osteoporotic fractures (hazard ratio [HR], 1.15; 95% confidence interval [CI], 0.93–1.42), hip fracture (HR, 0.90; 95% CI, 0.56–1.43), or any fracture (HR, 1.06; 95% CI, 0.88–1.28) compared with the general population. Conclusion Higher baseline BMI, BMD, and lower prevalence of prior fracture at baseline may offset the adverse effects of AI exposure. Although confirmatory data from large cohort studies are required, our findings challenge the view that all women with breast cancer initiating AI therapy should be considered at high risk for fractures. Implications for Practice In a population-based observational registry that included 1,775 patients initiating long-term aromatase inhibitor therapy, risk for major osteoporotic fracture, hip fracture, or any fracture was similar to the general population. Higher baseline body mass index, bone mineral density, and lower prevalence of prior fracture at baseline may offset the adverse effects of aromatase inhibitor exposure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".