Arthritis Increases the Risk for Fractures — Results from the Women’s Health Initiative
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between arthritis and fracture. METHODS: Women were classified into 3 self-reported groups at baseline: no arthritis (n = 83,295), osteoarthritis (OA; n = 63,402), and rheumatoid arthritis (RA; n = 960). Incident fractures were self-reported throughout followup. Age-adjusted fracture rates by arthritis category were generated, and the Cox proportional hazards model was used to test the association between arthritis and fracture. RESULTS: After an average of 7.80 years, 24,137 total fractures were reported including 2559 self-reported clinical spinal fractures and 1698 adjudicated hip fractures. For each fracture type, age-adjusted fracture rates were highest in the RA group and lowest in the nonarthritic group. After adjustment for several covariates, report of arthritis was associated with increased risk for spine, hip, and any clinical fractures. Compared to the nonarthritis group, the risk of sustaining any clinical fracture in the OA group was HR 1.09 (95% CI 1.05, 1.13; p < 0.001) and HR 1.49 (95% CI 1.26, 1.75; p < 0.001) in the RA group. The risk of sustaining a hip fracture was not statistically increased in the OA group (HR 1.11; 95% CI 0.98, 1.25; p = 0.122) compared to the nonarthritis group; however, the risk of hip fracture increased significantly (HR 3.03; 95% CI 2.03, 4.51; p < 0.001) in the RA group compared to the nonarthritis group. CONCLUSION: The increase in fracture risk confirms the importance of fracture prevention in patients with RA and OA.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".