From Theory to Practice: Bone Health in Women with Early Breast Cancer Treated with Aromatase Inhibitors
Bibliographic record
Abstract
Aromatase inhibitors (AI) are extensively used as adjuvant endocrine therapy in post-menopausal women with hormone receptor-positive early breast cancer (HR+ EBC), but their impact on bone health is not negligible. This work aimed to assess bone loss, fracture incidence, and risk factors associated with these events, as well as the prognostic influence of fractures. We have conducted a retrospective cohort study of women with HR+ EBC under adjuvant therapy with AI, during a 3-year period. Four-hundred-and-fifty-one eligible women were reviewed (median age 68 years). Median time under AI was 40 months. A fracture event occurred in 8.4%, mostly in the radium and femoral neck and in older women (mean 74 vs. 68 years, p = 0.006). Age (OR 1.01, 95% CI 1.01–1.07, p = 0.024) and time under AI (OR 1.02, 95% CI 1.00–1.04, p = 0.037) were independent predictors of fracture, with a fair discrimination (AUC 0.71). Analysis of disease-free survival according to fracture event varied between groups, disfavoring the fracture cohort (at 73 months, survival 78.6%, 95% CI, 47.6–92.4 vs. 95.6%, 95% CI, 91.2–97.8, p = 0.027). The multivariate model confirmed the prognostic impact of fracture occurrence (adjusted HR of 3.17, 95% CI 1.10–9.11; p = 0.032). Bone health is often forgotten, despite its great impact in survivorship. Our results validate the pathophysiologic link between EBC and bone metabolism, which translates into EBC recurrence. Further research in this area may help refine these findings. Moreover, early identification of women at higher risk for fractures is warranted.
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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 | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".