Occupational interstitial lung diseases
Bibliographic record
Abstract
Millions of workers are exposed to substances known to cause occupational interstitial lung diseases (ILDs), particularly in developing countries. However, the burden of the disease is likely to be underestimated due to under-recognition, under-reporting or both. The diagnosis of occupational ILD requires a high level of suspicion and a thorough occupational history, as occupational and non-occupational ILDs may be clinically, functionally and radiologically indistinguishable, leading to delayed diagnosis and inappropriate management. A potential occupational aetiology should always be considered in the differential diagnosis of ILD, as removal from the workplace exposure, with or without treatment, is a key therapeutic intervention and may lead to significant improvement. In this article, we provide an overview of the 'traditional' inorganic dust-related ILDs but also address idiopathic pulmonary fibrosis and the immunologically mediated chronic beryllium disease, sarcoidosis and hypersensitivity pneumonitis, with emphasis on the importance of surveillance and prevention for reducing the burden of these conditions. To this end, health-care professionals should be specifically trained about the importance of occupational exposures as a potential cause 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".