Patient-level factors predictive of interstitial lung disease in rheumatoid arthritis: a systematic review
Bibliographic record
Abstract
Objective Interstitial lung disease (ILD) is an important cause of mortality in some patients with rheumatoid arthritis (RA). Patient-level factors may predict which patients with RA are at the highest risk of developing ILD and are therefore candidates for screening for this complication of the underlying disease. Methods A systematic literature review was performed using PubMed, Embase and Scopus over a 10-year period up to July 2021. Publications reporting patient-level factors in patients with RA with and without ILD that were assessed before development of ILD (or were unchanged over time and therefore could be extrapolated to before development of ILD) were retrieved for assessment of evidence. Genetic variation in MUC5B and treatment with methotrexate were not included in the assessment of evidence because these factors have already been widely investigated for association with ILD. Results We found consistent associations of age, sex, smoking status and autoantibodies with development of ILD. For biomarkers such as Krebs von den Lungen 6, which have been shown to be diagnostic for ILD, there were no publications meeting criteria for this study. Conclusions This analysis provides an initial step in the identification of patient-level factors for potential development of a risk algorithm to identify patients with RA who may be candidates for screening for ILD. The findings represent a useful basis for future research leading to an improved understanding of the disease course and improved care for patients with RA at risk of development and progression of ILD.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".