110 Investigating a cancer cluster in Ontario, Canada using historical occupational hygiene data to evaluate risk of occupational cancers
Bibliographic record
Abstract
Abstract In 2022, the province of Ontario in Canada had 95,000 cancer cases diagnosed in a population of 15.11 million. The 16 most common occupational carcinogens cause 3,000 cancer diagnoses per year in Ontario. However, there is a lack of awareness of occupational cancers, with just 400 claims submitted on average per year to Ontario’s workers’ compensation insurance agency, of which 170 claims are accepted. A labour union approached the Occupational Health Clinics for Ontario Workers, Inc. (OHCOW) with a request to investigate a potential cancer cluster among a cohort of workers/retirees with exposures to blacksmithing, welding fumes, and diesel exhaust. OHCOW is a not-for-profit labour governed, worker-based network with a team of dedicated health professionals including 18 occupational hygienists. Occupational hygienists at OHCOW are trained to do retrospective exposure assessments using a combination of published data, databases of exposures, and when available, employer occupational hygiene data. Using occupational hygiene reports from the 1970s to 2010s, an occupational hygienist analyzed the workers’ exposures to IARC Group 1 carcinogens to investigate the cluster. This presentation will summarize the challenges of using historical data, the application of current evidence-based occupational exposure limits, the importance of referring to peer-reviewed published literature, and finally the outcomes of the workers’ compensation submissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".