Prevalence, imaging patterns and risk factors of interstitial lung disease in connective tissue disease: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Interstitial lung disease (ILD) is a frequent manifestation of connective tissue disease (CTD) with substantial variability in prevalence and outcomes reported across CTD subtypes. This systematic review summarises the prevalence, risk factors and ILD patterns on chest computed tomography of CTD-ILD. METHODS: A comprehensive search was performed in Medline and Embase to identify eligible studies. Meta-analyses were completed using a random effects model to determine the pooled prevalence of CTD-ILD and ILD patterns. RESULTS: 11 582 unique citations were identified with 237 articles included. Pooled prevalence of ILD was 11% in rheumatoid arthritis (95% CI 7-15%), 47% in systemic sclerosis (44-50%), 41% in idiopathic inflammatory myositis (33-50%), 17% in primary Sjögren's syndrome (12-21%), 56% in mixed connective tissue disease (39-72%) and 6% in systemic lupus erythematosus (3-10%). Usual interstitial pneumonia was the most prevalent ILD pattern in rheumatoid arthritis (pooled prevalence of 46%), while nonspecific interstitial pneumonia was the most common ILD pattern in all other CTD subtypes (pooled prevalence range 27-76%). Across all CTDs with available data, positive serology and higher inflammatory markers were risk factors for development of ILD. DISCUSSION: We identified substantial variability in ILD across CTD subtypes suggesting that CTD-ILD is too heterogenous to be considered a single entity.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".