Performance of FRAX in Women with Breast Cancer Initiating Aromatase Inhibitor Therapy: A Registry-Based Cohort Study
Bibliographic record
Abstract
FRAX was developed to predict 10-year probability of major osteoporotic fracture (MOF) and hip fracture in the general population. Aromatase inhibitors (AI) used in breast cancer induce loss in bone mineral density (BMD) and are reported to increase fracture risk. AI exposure is not a direct input to FRAX but is captured under "secondary osteoporosis". To inform use of FRAX in women treated with AI, we used a population-based registry for the Province of Manitoba, Canada, to identify women aged ≥40 years initiating AI for breast cancer with at least 12 months' AI exposure (n = 1775), women with breast cancer not receiving AI (n = 1016), and women from the general population (n = 34,205). Among AI users, fracture probability estimated without BMD (AI use coded as secondary osteoporosis) significantly overestimated risk (10-year observed/predicted ratio 0.56, 95% confidence interval [CI] 0.45-0.68; 10-year hip fracture observed/predicted ratio 0.33, 95% CI 0.18-0.49). However, when BMD was included in the fracture probability, there was no significant difference between observed and predicted fracture risk. In Cox proportional hazards models, FRAX stratified risk of MOF, hip, and any fracture equally well in all subgroups (p-interaction >0.1). When adjusted for FRAX score without BMD, with AI use coded as secondary osteoporosis, AI users were at significantly lower risk for MOF (hazard ratio [HR] = 0.78, 95% CI 0.64-0.95), hip fracture (HR = 0.46, 95% CI 0.29-0.73) and any fracture (HR = 0.75, 95% CI 0.63-0.89). AI use was no longer significantly associated with fractures when AI use was not entered as secondary osteoporosis in FRAX without BMD or when BMD was included in the FRAX calculation. In conclusion, FRAX scores stratify fracture risk equally well in women receiving AI therapy as in non-users, but including secondary osteoporosis as a risk factor for AI users overestimates fracture risk. Our results call this practice into question. © 2019 American Society for Bone and Mineral Research.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".