Work-exacerbated asthma in a workers' compensation population
Bibliographic record
Abstract
BACKGROUND: Asthma is becoming more prevalent with large numbers of individuals suffering from work-exacerbated asthma. AIMS: To examine the characteristics of workplace exposures and working days lost in relation to work-exacerbated asthma (WEA) in a workers' compensation population. METHODS: An analysis of accepted workers' compensation asthma claims in Ontario over a 5-year period. Claims among the top three industry groups were categorized based on working time lost of 1 day or less, 2-5 days and 6 days or more. Attributable agents were subdivided into dusts, smoke, chemicals and sensitizers. RESULTS: Among the asthma claims, 72% (645) fulfilled criteria for WEA from their history. The commonest industry groups were services, education and health care, with 270 claims that met our analysis requirements. Within these industry groups, education had a lower proportion of workers with short exacerbations (missing 1 day or less: 27%) while the health care industry had a higher than expected proportion of short exacerbations (55%). The agents to which WEA was attributed differed across the groups, with dusts having the highest proportion in the education group (65%), smoke in the service industry (34%) and sensitizers in health care (41%). Those agents more commonly attributed to exacerbations tended to have lower rates of prolonged exacerbation compared with less commonly involved agents. CONCLUSIONS: The morbidity of WEA and the type of agents to which it was attributed varied between industry groups.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".