The American Society for Bone and Mineral Research Task Force on clinical algorithms for fracture risk report
Bibliographic record
Abstract
Using race and ethnicity in clinical algorithms potentially contributes to health inequities. The American Society for Bone and Mineral Research (ASBMR) Professional Practice Committee convened the ASBMR Task Force on Clinical Algorithms for Fracture Risk to determine the impact of race and ethnicity adjustment in the US Fracture Risk Assessment Tool (US-FRAX). The Task Force engaged the University of Minnesota Evidence-based Practice Core to conduct a systematic review investigating the performance of US-FRAX for predicting incident fractures over 10 years in Asian, Black, Hispanic, and White individuals. Six studies from the Women's Health Initiative (WHI) and Study of Osteoporotic Fractures (SOF) were eligible; cohorts only included women and were predominantly White (WHI > 80% and SOF > 99%), data were not consistently stratified by race and ethnicity, and when stratified there were far fewer fractures in Black and Hispanic women vs White women rendering area under the curve (AUC) estimates less stable. In the younger WHI cohort (n = 64 739), US-FRAX without bone mineral density (BMD) had limited discrimination for major osteoporotic fracture (MOF) (AUC 0.53 (Black), 0.57 (Hispanic), and 0.57 (White)); somewhat better discrimination for hip fracture in White women only (AUC 0.54 (Black), 0.53 (Hispanic), and 0.66 (White)). In a subset of the older WHI cohort (n = 23 918), US-FRAX without BMD overestimated MOF. The Task Force concluded that there is little justification for estimating fracture risk while incorporating race and ethnicity adjustments and recommends that fracture prediction models not include race or ethnicity adjustment but instead be population-based and reflective of US demographics, and inclusive of key clinical, behavioral, and social determinants (where applicable). Research cohorts should be representative vis-à-vis race, ethnicity, gender, and age. There should be standardized collection of race and ethnicity; collection of social determinants of health to investigate impact on fracture risk; and measurement of fracture rates and BMD in cohorts inclusive of those historically underrepresented in osteoporosis 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 | 0.136 | 0.342 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".