Inhalational exposures associated with risk of interstitial lung disease: a systematic review and meta-analysis
Bibliographic record
Abstract
RATIONALE: Inhalational exposures are associated with risk of developing interstitial lung disease (ILD), yet the relationship between specific exposures and ILD is poorly characterised. OBJECTIVE: Identify inhalational exposures associated with ILD and estimate the effects of exposures on ILD risk. METHODS: MEDLINE and EMBASE databases were searched from 1990 to 2022 to identify inhalational exposures associated with ILD diagnosis. ILDs where causality is well-established (hypersensitivity pneumonitis, pneumoconiosis) and sarcoidosis were excluded. Two independent reviewers screened abstracts with full-text review and data extraction of eligible studies. Where possible, data were pooled and multilevel meta-analysis was specified using a random effects model. Sources of heterogeneity and risk of bias were assessed. MAIN RESULTS: 96 studies were included in the systematic review, representing 40 819 116 subjects (295 167 had ILD, 40 523 949 controls). For the meta-analysis, 54 studies were included (40 490 793 subjects: 273 899 ILD, 40 216 894 controls). Exposures associated with significantly increased ILD risk included smoking (OR 1.69, 95% CI 1.47 to 1.94), organic exposures (OR 1.56, 95% CI 1.12 to 2.16), metals (OR 1.52, 95% CI 1.07 to 2.16), dust (OR 1.45, 95% CI 1.20 to 1.76) and asbestos (OR 1.53, 95% CI 1.08 to 2.15). Silica and fumes had positive associations with ILD that trended towards significance. CONCLUSIONS: This systematic review and multilevel meta-analysis is the first to comprehensively assess the effect of inhalational exposures on overall risk of ILD, with multiple putative exposures identified. Future work should investigate novel occupational exposures associated with ILD, characterise the gene-environment interaction and develop preventative strategies. PROSPERO REGISTRATION NUMBER: CRD42022292908.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".