FRAX underestimates fracture risk in patients with diabetes
Bibliographic record
Abstract
The study objective was to determine whether diabetes is a risk factor for incident hip or major osteoporotic fractures independent of the WHO fracture risk assessment tool (FRAX). Men and women with diabetes (n = 3518) and nondiabetics (n = 36,085) aged ≥50 years at the time of bone mineral density (BMD) testing (1990 to 2007) were identified in a large clinical database from Manitoba, Canada. FRAX probabilities were calculated, and fracture outcomes to 2008 were established via linkage with a population-based data repository. Multivariable Cox proportional hazards models were used to determine if diabetes was associated with incident hip fractures or major osteoporotic fractures after controlling for FRAX risk factors. Mean 10-year probabilities of fracture were similar between groups for major fractures (diabetic 11.1 ± 7.2 versus nondiabetic 10.9 ± 7.3, p = 0.116) and hip fractures (diabetic 2.9 ± 4.4 versus nondiabetic 2.8 ± 4.4, p = 0.400). Diabetes was a significant predictor of subsequent major osteoporotic fracture (hazard ratio [HR] = 1.61, 95% confidence interval [CI] 1.42-1.83) after controlling for age, sex, medication use, and FRAX risk factors including BMD. Similar results were seen after adjusting for FRAX probability directly (HR = 1.59, 95% CI 1.40-1.79). Diabetes was also associated with significantly higher risk for hip fractures (p < 0.001). Higher mortality from diabetes attenuated but did not eliminate the excess fracture risk. FRAX underestimated observed major osteoporotic and hip fracture risk in diabetics (adjusted for competing mortality) but demonstrated good concordance with observed fractures for nondiabetics. We conclude that diabetes confers an increased risk of fracture that is independent of FRAX derived with BMD. This suggests that diabetes might be considered for inclusion in future iterations of FRAX.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".