Validating the Fracture Risk Assessment Tool Score in a US Population-Based Study of Patients With Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: The World Health Organization fracture risk assessment tool (FRAX) algorithm for risk prediction of major osteoporotic and hip fractures accounts for several risk factors, including rheumatoid arthritis (RA), since individuals with RA have an excess burden of fractures. FRAX has not been validated in population-based RA cohorts in the US. We aimed to determine the accuracy of FRAX predictions for individuals with RA in the US. METHODS: This retrospective population-based cohort study included residents of Olmsted County, Minnesota, who were followed until death, migration, or last medical record review. Each patient with RA (1987 American College of Rheumatology criteria met in 1980-2007, age 40-89 years) was matched 1:1 on age and sex to an individual without RA from the same underlying population. Ten-year predictions for major osteoporotic and hip fractures were estimated using the FRAX tool. Fractures were ascertained through follow-up, truncated at 10 years. Standardized incidence ratios (SIRs) and 95% CI were calculated to compare observed and predicted fractures. RESULTS: The study included 662 patients with RA and 658 non-RA comparators (66.8% vs 66.9% female and a mean age of 60.6 vs 60.5 years, respectively). Among patients with RA, 76 major osteoporotic fractures and 21 hip fractures were observed during follow-up (median follow-up: 9.0 years) compared to 67.0 predicted major osteoporotic fractures (SIR 1.13, 95% CI 0.91-1.42) and 23.3 predicted hip fractures (SIR 0.90, 95% CI 0.59-1.38). The observed and predicted major osteoporotic and hip fracture risks were similar for patients with RA and non-RA comparators. CONCLUSION: The FRAX tool is an accurate method for estimating major osteoporotic and hip fracture risk in patients with RA.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
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".