Factors associated with secondary coronary artery disease in rheumatoid arthritis patients: A systematic review and meta‐analysis based on observational studies
Bibliographic record
Abstract
OBJECTIVE: The main objective of this systematic review was to investigate the factors influencing the development of coronary artery disease (CAD) in patients with rheumatoid arthritis (RA). METHODS: PubMed, Embase, Web of Science, Wan Fang Date, CBM, CNKI, and VIP databases were systematically searched to select the relevant literature. The quality of the incorporated studies was assessed with reference to the Newcastle-Ottawa Scale. Stata16 was adopted to summarise the odds ratios, risk ratios, hazard ratios, and 95% confidence intervals for meta-analysis. RESULTS: A total of 29 studies were included in this analysis, wherein the average age of RA patients was 50.5-81 years and the proportion of women was 44.4%-92%. The present meta-analysis suggested that increased CAD risk in RA patients was associated with age, male gender, smoking, glucocorticoids, Health Assessment Questionnaire scores, hyperlipidaemia, hypertension, diabetes, and C-reactive protein concentration. CONCLUSION: The present systematic review revealed the influencing factors of secondary CAD in RA patients, some of which could reduce the risk of secondary CAD through effective interventions, such as smoking cessation, exercise, and medications. However, the effects of age, RA severity, and different medication subgroups on CAD risk stratification warrant further investigation.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".