Is Rheumatoid Arthritis a Risk Factor for Fractures: A Systematic Review of Observational Studies
Bibliographic record
Abstract
AIM: The primary objective was to assess the risk of fractures in adults with RA compared with controls from the general population. The review also assessed an increased risk of fractures in RA patients when accounting for steroid use, RA disease severity or functional impairment. METHODS: Citations were screened from MEDLINE, EMBASE, Cochrane Database of Systematic Reviews and CINAHL. Included citations were written in English, including adult patients at least 18 years of age and compared fracture incidence or prevalence between RA patients and a control group. Case-control, cohort and cross-sectional studies were included. RESULTS: There were a total of 3451 citations; after application of the inclusion criteria, 17 studies were selected. In 14 of the 17 studies, there was an increase in the risk of fracture in RA patients compared to controls. In studies that evaluated for glucocorticoid use, four of 13 demonstrated an increased risk of fracture with glucocorticoid use, however, only two of these four studies specifically assessed glucocorticoid use amongst patients with RA. In studies that analyzed RA severity or functional impairment, two of seven demonstrated disease severity or impairment as a risk factor for fracture. There was marked study heterogeneity in terms of patient and fracture characteristics, which was a limitation of the analysis that impeded the ability to make direct comparisons. CONCLUSION: The risk of fracture in RA patients is elevated when compared to the general population, although the etiology of the increased risk remains to be elucidated.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".