Prevention of occupational asthma—practical implications for occupational physicians
Bibliographic record
Abstract
BACKGROUND: Occupational factors have been estimated to contribute to approximately 10% of adult-onset asthma and occupational asthma (OA) is one of the most common occupational lung diseases in industrialized areas. Persistent asthma frequently occurs with significant socio-economic impacts. METHODS: A literature search was performed using PubMed. The key term searched was occupational asthma combined with prevention. RESULTS: Primary prevention has been effective for OA related to natural rubber latex, and may have reduced the incidence of diisocyanate-induced asthma. Medical health surveillance has been effective in settings such as the detergent enzyme industry, workers exposed to complex platinum salts and likely for diisocyanate workers in Ontario. Tertiary prevention is still required for workers with OA and can improve prognosis. CONCLUSIONS: OA is potentially preventable. Sufficient studies have demonstrated the rationale and benefit of primary preventive strategies. Medical health surveillance programs combined with occupational hygiene measures and worker education have been associated with improved outcomes but further studies are needed to understand the optimum frequency and measures for such programs and to identify the separate contribution of the components. Until primary and secondary prevention is better understood and implemented, there will also remain a need for tertiary preventive measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".