Fracture prediction in rheumatoid arthritis: validation of FRAX with bone mineral density for incident major osteoporotic fractures
Bibliographic record
Abstract
OBJECTIVES: FRAX uses clinical risk factors, with or without BMD, to calculate 10-year fracture risk. RA is a risk factor for osteoporotic fracture and a FRAX input variable. FRAX predates the current era of RA treatment. We examined how well FRAX predicts fracture in contemporary RA patients. METHODS: Administrative data from patients receiving BMD testing were linked to the Manitoba Population Health Research Data Repository. Observed cumulative 10-year major osteoporotic fracture (MOF) probability was compared with FRAX-predicted 10-year MOF probability with BMD for assessing calibration. MOF risk stratification was assessed using Cox regression. RESULTS: RA patients (n = 2099, 208 with incident MOF) and non-RA patients (n = 2099, with 165 incident MOF) were identified. For RA patients, FRAX-predicted 10-year risk was 13.2% and observed 10-year MOF risk was 13.2% (95% CI 11.6, 15.1). The slope of the calibration plot was 0.67 (95% CI 0.53, 0.81) in those with RA vs 0.98 (95% CI 0.61, 1.34) in non-RA patients. Risk was overestimated in RA patients with high FRAX scores (>20%), but FRAX was well calibrated in other groups. FRAX stratified risk in those with and without RA [hazard ratio (HR) 1.52 (95% CI 1.25, 1.72) vs 2.00 (95% CI 1.73, 2.31)], with slightly better performance in the latter (P for interaction = 0.004). CONCLUSIONS: FRAX predicts fracture risk in contemporary RA patients but may slightly overestimate risk in those already at high predicted risk. Thus the current FRAX tool continues to be appropriate for fracture risk assessment in RA patients.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".