The Utility of an Occupational Contact Dermatitis Patch Test Database in the Analysis of Workplace Prevention Activities in Toronto, Canada
Bibliographic record
Abstract
BACKGROUND: Occupational skin diseases are common suggesting that there are still gaps in workplace prevention. Patch test surveillance systems provide an opportunity to collect work related information in addition to clinical information and patch test results. OBJECTIVES: To examine 5 years of data related to workplace prevention by industry sector in a patch test surveillance database for workers with a diagnosis of occupational contact dermatitis. METHODS: The study was approved by the Research Ethics Board of St Michael's Hospital. Information including demographics, clinical history, healthcare utilization, and workplace characteristics and prevention practices in addition to patch test results was collected from consenting patients. RESULTS: Workers in the healthcare and manufacturing sectors were more likely to report workplace training including skin protection training, whereas those in services and construction were less likely to report training. CONCLUSIONS: Collecting basic workplace information with patch test surveillance databases can inform the occupational health and safety system about prevention practices in the workplace and identify areas for focussed intervention.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.002 | 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".