Occupational risk factors associated with work-exacerbated asthma in Quebec
Bibliographic record
Abstract
BACKGROUND: There is limited information regarding the occupational exposures of subjects with a diagnosis of work-exacerbated asthma (WEA). OBJECTIVES: To: (1) identify potential specific occupational, chemical, biological and physical agents associated with incident cases of WEA and (2) compare these agents with occupational exposures of occupational asthma (OA) and non-work-related asthma (NWRA) cases. METHODS: Subjects were workers with work-related asthma (WRA) or NWRA referred between 2005 and 2008 to two Quebec clinics specialised in the field of WRA. Specific inhalation challenges were performed to differentiate OA from WEA. Work exposures were assessed using a detailed occupational questionnaire. Exposures to 41 chemical and biological agents were coded in a semiquantitative way according to a combination of indices for concentration in workplace air, frequency and confidence of exposure by an occupational hygienist expert in occupational exposure coding. This expert was blind to the medical status of WEA, OA or NWRA. Five physical agents were coded on a yes/no scale. RESULTS: 153 subjects were enrolled (53 WEA, 67 OA and 33 NWRA). WEA cases were significantly more exposed to ammonia, engine exhaust fumes, silica, mineral fibres, aerosol propellants and solvents, and significantly less exposed to animal derived dust and enzymes than were OA cases. Exposure to physical conditions did not differ between WEA and OA. CONCLUSIONS: Exposures associated with WEA differ from those associated with OA in this study. A proportion of subjects with WEA may suffer from low-dose irritant asthma, which remains a hypothesis to be tested.
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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) | 0.004 | 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".