Association of Sinusitis and Upper Respiratory Tract Diseases With Incident Rheumatoid Arthritis: A Case-control Study
Bibliographic record
Abstract
Objective We aimed to determine whether specific respiratory tract diseases are associated with increased rheumatoid arthritis (RA) risk. Methods This case-control study within the Mass General Brigham Biobank matched newly diagnosed RA cases to 3 controls on age, sex, and electronic health record history. We identified RA using a validated algorithm and confirmed by medical record review. Respiratory tract disease exposure required 1 inpatient or 2 outpatient codes at least 2 years before the index date of RA clinical diagnosis or matched date. Logistic regression models calculated ORs for RA with 95% CIs, adjusting for confounders. We then stratified by serostatus (“seropositive” was positive rheumatoid factor and/or anticitrullinated protein antibodies) and smoking. Results We identified 741 RA cases and 2223 controls (both median age 55, 76% female). Acute sinusitis (OR 1.61, 95% CI 1.05–2.45), chronic sinusitis (OR 2.16, 95% CI 1.39–3.35), and asthma (OR 1.39, 95% CI 1.03–1.87) were associated with increased risk of RA. Acute respiratory tract disease burden during the preindex exposure period was also associated with increased RA risk (OR 1.30 per 10 codes, 95% CI 1.08–1.55). Acute pharyngitis was associated with seronegative (OR 1.68, 95% CI 1.02–2.74) but not seropositive RA; chronic rhinitis/pharyngitis was associated with seropositive (OR 2.46, 95% CI 1.01–5.99) but not seronegative RA. Respiratory tract diseases tended towards higher associations in smokers, especially > 10 pack-years (OR 1.52, 95% CI 1.02–2.27, P = 0.10 for interaction). Conclusion Acute and chronic sinusitis, pharyngitis, and acute respiratory burden increased RA risk. The mucosal paradigm of RA pathogenesis may involve the upper respiratory tract.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".