12 Exposure to Metals and Particles in Welding and Episodes of Asthma/Wheeze and Rhinitis: a Canadian Cohort Study.
Bibliographic record
Abstract
Abstract Introduction Welders are exposed through welding fume to particles and metals, including nickel and chromium, that may act as respiratory sensitizers. In a Canadian study of male and female welders we investigated the relation of modelled welding exposures to episodes of asthma/wheeze and rhinitis. Methods Welders who had been registered in an apprenticeship were recruited and followed up at 6-month intervals for up to five years. Women were recruited from across Canada, men just from Alberta. At each contact participants were asked about their health and specifically about symptoms of asthma and rhinitis and age these had first occurred. A complete job history was taken and at each contact after recruitment the participant completed a detailed welding task questionnaire. A job exposure matrix was developed, reflecting type of welding, base metal and consumables. This was validated against urinary metal concentration. Results 1001 welders, including 447 women, were recruited. In bivariate analyses, hours of welding/week, hours grinding/day and estimated exposures to total particles, manganese, nickel and aluminum were related to episodes of asthma/wheeze. Hours welding, grinding and nickel exposure were related to rhinitis. In multivariable models, including all exposures and confounders, the number of hours welding/week and hours grinding/day were related to asthma/wheeze episodes and hours grinding and nickel exposure to rhinitis. Conclusions Duration of welding tasks was related to episodes of asthma/wheeze and rhinitis. There was less evidence that estimated metal concentrations were associated with respiratory ill-health, having adjusted for hours welding.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".