Risk factors for incidence of interstitial lung disease in patients with rheumatoid arthritis: a systematic review and meta-analysis
Bibliographic record
Abstract
Objectives This study aimed at identifying risk factors for the incidence of interstitial lung disease in patients with rheumatoid arthritis (RA-ILD) by a systematic review and meta-analysis. Methods Information sources: studies published by March 2021 were searched in PubMed, Web of Science, MEDLINE, EMBASE, Cochrane Library and Scopus databases. Eligibility criteria: cohort studies or nested case-control studies that reported OR or HR of risk factors for RA-ILD were included. Two researchers independently screened the studies and extracted data. Synthesis of results: the relative risks (RRs) were introduced to measure the association across studies. Risk bias: quality assessments of included studies were performed using the Newcastle-Ottawa Scale. Based on the result of heterogeneity, the random-effects model or fixed-effects model was chosen in the meta-analysis. Furthermore, a sensitivity analysis was conducted to identify the origins of heterogeneity, and publication bias was evaluated for the factors with no less than five included studies by funnel plots and Egger’s test. Results Among 3075 identified articles, 12 studies met the inclusion criteria. 17 risk factors were included in the meta-analysis. Male (RR 1.94, 95% CI 1.33 to 2.85, p<0.001), elder age (>60 years, RR 1.42, 95% CI 1.05 to 1.94, p=0.02), older RA onset age (RR 1.05, 95% CI 1.01 to 1.10, p=0.02), smoking (RR 1.37, 95% CI 1.09 to 1.71, p=0.006), lung complications (RR 2.72, 95% CI 1.24 to 5.95, p=0.01), rheumatoid nodule (RR 1.85, 95% CI 1.36 to 2.51, p<0.001), leflunomide usage (RR 1.41, 95% CI 1.02 to 1.96, p=0.04) were identified as risk factors of RA-ILD. Conclusion Physicians should be aware that patients with RA with the above risk factors are likely to develop RA-ILD, and perform close ILD screening during follow-ups so that the patients can be early diagnosed and treated, and achieve improved prognosis.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.059 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".