The Risk of Rheumatic Disorders Among Patients With Rhinosinusitis: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Emerging evidence suggests a possible link between rhinosinusitis and systemic rheumatic diseases; however, no meta-analysis has comprehensively examined this association to date. We aimed to investigate if patients with rhinosinusitis have a predisposition to unmasking rheumatic diseases compared to individuals without rhinosinusitis. METHODS: A comprehensive search in MEDLINE, Embase, Cochrane Library, and Web of Science was conducted until February 2025 for studies characterizing rheumatic disease incidence, prevalence, and risk in cohorts of rhinosinusitis patients. The search was limited to records authored in English and involved combining both rhinitis and sinusitis Medical Subject Headings (MeSH) terms. Relative risk and prevalence data were pooled using random-effects models. RESULTS: Nine studies with 86,081 rhinosinusitis patients were included. Chronic rhinosinusitis (CRS) was significantly associated with increased risk of rheumatoid arthritis (RA) (odds ratio [OR]: 1.70; 95% CI: 1.44-2.00; p < 0.00001), systemic lupus erythematosus (OR: 1.61; 95% CI: 1.25-2.08; p = 0.0002), and ankylosing spondylitis (OR: 1.48; 95% CI: 1.26-1.72; p < 0.00001). Acute rhinosinusitis (ARS) showed weaker associations, notably with seronegative RA. Rheumatic disease prevalence in rhinosinusitis patients was highest for RA (10%, 95% CI: 8.2-13). CONCLUSION: Rhinosinusitis, particularly CRS, is associated with several rheumatic diseases. Mechanisms of mucosal immune dysregulation associated with rhinosinusitis may contribute to, or act in parallel with, the pathogenesis of systemic autoimmunity. Management of CRS (surgical or immunomodulatory) may lead to the sentinel presentation and subsequent identification of previously undiagnosed systemic autoimmune conditions. Clinicians should maintain a high index of suspicion for early autoimmune symptoms in rhinosinusitis patients.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".