Screening for Rheumatoid Arthritis–Associated Interstitial Lung Disease: Current Evidence and Next Steps Needed for Routine Clinical Use
Bibliographic record
Abstract
Interstitial lung disease (ILD), characterized by pulmonary fibrosis and/or inflammation, is a common and severe extraarticular manifestation of rheumatoid arthritis (RA). RA-ILD is associated with reduced quality of life and increased mortality. Among people with RA, up to 15% develop clinically significant ILD, and even more have subclinical disease (radiologic abnormalities without symptoms). The most common RA-ILD patterns on chest high-resolution computed tomography (CT) imaging are usual interstitial pneumonia (UIP; the prototypic fibrotic subtype) and nonspecific interstitial pneumonia (the subtype characterized by inflammation). In this narrative review, we detail the current state of evidence for RA-ILD screening and the next steps needed to justify screening in some subgroups. Some current or former smokers with RA may currently qualify for lung cancer screening with low-dose CT imaging, which may also detect ILD. The 2023 American College of Rheumatology (ACR)/American College of Chest Physicians (CHEST) guideline for screening and monitoring of ILD conditionally recommended screening people with RA with an ILD risk factor (ie, male sex, older age, smoking, RA-related autoantibody elevation, obesity, and high RA disease activity). Several genetic and blood biomarkers are associated with RA-ILD. The MUC5B promoter variant is the strongest genetic risk factor for RA-ILD, specifically the UIP subtype. Proposed screening strategies show promise for accurately detecting RA-ILD; however, there has been less research on other consequences of screening for RA-ILD, including cost, anxiety, radiation exposure, incidental findings, and downstream clinical follow-up. Clinical trials are needed to identify an intervention that alters the natural history for those found to have subclinical RA-ILD on screening.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".