Environmental and occupational exposures in interstitial lung disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: We highlight recent advances in the understanding of how environmental and occupational exposures increase the risk of developing interstitial lung disease (ILD), and how to evaluate a patient for potential exposures. RECENT FINDINGS: A review of emerging literature suggests that environmental and occupational exposures can be directly causal, as in the case of the pneumoconioses and smoking-related ILDs, or one of many contributors to disease, as in the case of idiopathic pulmonary fibrosis (IPF). Regardless of the level of association, exposures are clearly prevalent across all ILD subtypes studied. SUMMARY: Inhalational exposures are increasingly recognized as an important component in the development of ILDs, and novel exposure-disease associations continue to be discovered. These exposures represent potential opportunities for further understanding the pathobiology of disease and for the prevention of these often progressive and debilitating disorders. Prospective, comprehensive data collection regarding occupational and environmental exposures are needed in ILD patients to fully elucidate specific antigens and their relationships to disease incidence and outcomes. Systematically collected exposure information will also inform potential interventions to remediate exposures and thus mitigate the course of frequently progressive and fatal diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".