Systematic Review of Bone Health in Older Women Treated with Aromatase Inhibitors for Early‐Stage Breast Cancer
Bibliographic record
Abstract
OBJECTIVES: To review data from randomized controlled trials (RCTs) that evaluate adverse bone outcomes in older women using aromatase inhibitors (AIs) for early-stage hormone receptor-positive breast cancer. DESIGN: Systematic review. SETTING: International RCTs referenced in Medline and EMBASE databases through August 1, 2011. PARTICIPANTS: Postmenopausal women with early-stage hormone receptor-positive breast cancer receiving adjuvant endocrine therapy. MEASUREMENTS: Fracture rates and changes in bone turnover markers and bone mineral density. RESULTS: Eleven RCTs were identified. The majority of trials included women with a mean age in the 60s; and women aged 75 and older and 80 and older were excluded from two studies. Fracture rates ranged from 0.9% to 11%, with AIs having a 1.5 times higher risk than tamoxifen or placebo. Fracture data were not systematically collected in many of these trials. In a small subpopulation of women, AIs were associated with higher markers of bone turnover and lower bone density. The relationship between age and fracture was not described. CONCLUSION: AIs are associated with low bone density and high fracture risk in women with a mean age in their early 60s. There is a paucity of data describing the effect of baseline fracture risk factors, particularly age, and the longer-term effects on bone health in older women. Future research is needed regarding baseline fracture risk, interventions, and long-term effects on bone in this vulnerable population to inform management decisions to optimize AI duration and ensure quality of life after breast cancer.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".