Demographic, Lifestyle, and Serologic Risk Factors for Rheumatoid Arthritis (RA)–associated Bronchiectasis: Role of RA-related Autoantibodies
Bibliographic record
Abstract
OBJECTIVE: To investigate demographic, lifestyle, and serologic risk factors for isolated rheumatoid arthritis (RA)-associated bronchiectasis (RA-BR) that is not a result of interstitial lung disease (ILD). METHODS: We performed a case-control study using patients with RA from the Mass General Brigham Biobank. We reviewed the records of all patients with RA meeting the 2010 American College of Rheumatology/European Alliance of Associations for Rheumatology criteria with computed tomography (CT) chest imaging to identify RA-BR cases and controls with RA and RA-related lung disease. For each patient, the CT chest imaging that was performed closest to enrollment was independently reviewed by 2 radiologists for the presence of RA-related lung diseases. Cases had clinical and radiologic evidence of RA-BR without interstitial lung abnormalities on imaging. Controls had RA and no evidence of bronchiectasis or ILD. We examined the associations between demographic, lifestyle, and serologic factors with RA-BR using multivariable logistic regression. RESULTS: , 95% CI 0.89-0.99), seropositive RA (OR 3.96, 95% CI 1.84-8.53), positive rheumatoid factor (OR 4.40, 95% CI 2.14-9.07), and positive anticyclic citrullinated peptide (OR 3.47, 95% CI 1.65-7.31). Higher titers of RA-related autoantibodies were associated with higher odds of RA-BR. CONCLUSION: Seropositivity, older age at RA diagnosis, and lower BMI at RA onset were associated with isolated bronchiectasis in RA that was not a result of ILD. These findings expand the list of potential risk factors for RA-BR and suggest a pathogenic link between airway inflammation and RA-related autoantibodies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".