Exposure to Occupational Inhalants and the Risk of Developing Rheumatoid Arthritis: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Objective Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by joint pain, swelling, and stiffness. Although smoking is a well‐established risk factor for RA, the role of occupational inhalants in RA development is less well recognized. This study aimed to systematically review and synthesize existing evidence on the association between occupational inhalants and the risk of developing RA. Methods We conducted a systematic review and meta‐analysis following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines, searching MEDLINE, Embase, and Web of Science from database inception to November 20, 2024. Eligible studies were cross‐sectional, were case‐control and cohort designs, were population‐based, reported original data on occupational inhalant exposures and RA, measured exposures, included a comparison group, and used reliable RA ascertainment methods. Studies relying solely on self‐reported RA or focusing on treatment, prognosis, sick leave, or death were excluded. Two reviewers independently conducted literature screening, data extraction, and risk‐of‐bias assessment using the Newcastle–Ottawa Scale. Random‐effects meta‐analyses with relative risk were performed for cohort and case‐control studies, and heterogeneity was assessed using the I 2 statistic. Results In total, 31 studies met inclusion criteria, and 25 were included in meta‐analyses across 10 types of occupational inhalants. Significant associations with RA risk were observed for exposure to silica, asbestos, solvents, pesticides, fertilizers, animal dust, and engine exhaust (relative risks ranging from 1.25 to 1.49). Moderate‐to‐high heterogeneity was observed in seven meta‐analyses. Conclusion Occupational inhalants are associated with increased RA risk, underscoring the importance of workplace prevention strategies and further research into biologic mechanisms.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.045 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".