Fragility Fractures Are Associated with an Increased Risk for Cardiovascular Events in Women and Men with Rheumatoid Arthritis: A Population-based Study
Bibliographic record
Abstract
OBJECTIVE: Women and men with rheumatoid arthritis (RA) have an increased risk for fragility fractures and cardiovascular disease (CVD), each of which has been reported to contribute to excess morbidity and mortality in these patients. Fragility fractures share similar risk factors for CVD but may occur at relatively younger ages in patients with RA. We aimed to determine whether a fragility fracture predicts the development of CVD in women and men with RA. METHODS: We studied a population-based cohort with incident RA from 1955 to 2007 and compared it with age- and sex-matched non-RA subjects. We identified fragility fractures and CVD events following the RA incidence/index date, along with relevant risk factors. We used Cox models to examine the association between fractures and the development of CVD, in which fractures and CVD risk factors were modeled as time-dependent covariates. RESULTS: There were 1171 subjects (822 women; 349 men) in each of the RA and non-RA cohorts. Over followup, there were 406 and 346 fragility fractures and 286 and 225 CVD events, respectively. The overall CVD risk was increased significantly for RA subjects following a fragility fracture (HR 1.81, 95% CI 1.38-2.37) but not for non-RA subjects (HR 1.18, 95% CI 0.85-1.63). Results were similar for women and men with RA. CONCLUSION: Fragility fractures in both women and men with RA are associated with an increased risk for CVD events and should raise an alert to clinicians to target these individuals for further screening and preventive strategies for CVD.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 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".