Total hip bone area affects fracture prediction with FRAX® in Canadian white women
Bibliographic record
Abstract
Context Areal bone mineral density (BMD) measurements are confounded by skeletal size. Hip BMD is an input to the FRAX® tool (Centre for Metabolic Bone Diseases, University of Sheffield, United Kingdom), but it is unknown whether performance is affected by hip area. Objective To examine whether fracture prediction by FRAX® is affected by hip area. Design and Setting Cohort study using a population-based BMD registry. Patients A total of 58,108 white women aged ≥40 years. Main Outcome Measures Incident major osteoporotic fracture (MOF; n = 4913) and hip fracture (n = 1369), stratified by total hip area quintile, before and after adjustment for hip axis length (HAL). Results Smaller hip area was associated with younger age and lower FRAX® scores, whereas incident fractures were greater in those with larger hip area (P for trend < 0.001). Larger hip area quintile increased risk for MOF and hip fracture when adjusted for FRAX® score with BMD (P for trend < 0.001). Each standard deviation increase in hip area was associated with greater risk for incident MOF [adjusted hazard ratio (HR), 1.08; 95% confidence interval (CI), 1.05 to 1.11] and hip fracture (HR, 1.16; 95% CI, 1.11 to 1.21), but not after adjustment for HAL. FRAX® with BMD underestimated MOF risk in the largest hip area quintile and underestimated hip fracture risk in the three largest hip area quintiles. Conclusions In Canadian white women, skeletal size based on hip area affects fracture risk assessment based on FRAX® score with BMD, with risk underestimated in those with larger hip areas. Including HAL in the risk assessment compensates for this confounding by skeletal size and provides for more accurate assessment of fracture risk.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".