Assessing contact dermatitis risk among Manitoba workers in the Manitoba Occupational Disease Surveillance System
Bibliographic record
Abstract
INTRODUCTION: This exploratory study aimed to assess contact dermatitis (CD) risk among workers using the Manitoba Occupational Disease Surveillance System (MODSS). METHODS: The MODSS linked accepted time-loss claims from the Workers' Compensation Board of Manitoba (2006-2019), with administrative health data from medical and hospital records (1996-2020). CD risk by occupation and industry (hazard ratio, 95% confidence intervals) was estimated using Cox proportional hazard models, adjusted for age and stratified by sex. RESULTS: Increased risk of new onset CD was observed among some occupations and industries with known skin irritants and allergens. Some occupations with known increased risks of CD remained elevated when removing the accepted WCB cases was performed, suggesting that all CD cases in these occupations may not show up in WCB statistics. Increased risk was also observed for occupations and industries with unknown exposures related to CD, whereas some groups known to be at risk of CD were not observed to have elevated risks in this cohort. DISCUSSION: The MODSS successfully identified some occupations and industries known to be at high risk of occupational CD, but not others. Some occupations not typically associated with work-related CD were also identified, which warrants further investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".