Cancer surveillance among workers in plastics and rubber manufacturing in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: Occupational exposure to agents in plastics and rubber manufacturing has been associated with elevated risk of certain cancers. We sought to evaluate cancer risk among workers employed in occupations and industries with these exposures as part of an ongoing surveillance programme in Ontario, Canada. METHODS: The Occupational Disease Surveillance System (ODSS) cohort was established using workers' compensation claims data and includes 2.18 million workers employed from 1983 to 2014. Workers were followed for site-specific cancer diagnoses in the Ontario Cancer Registry through 2016. Cox proportional hazard models were used to estimate adjusted HR and 95% CI. RESULTS: We identified 81 127 workers employed in plastics and rubber manufacturing industries or materials processing and product fabricating occupations. Compared with all other women in the ODSS, those in materials processing occupations had an elevated rate of lung cancer (HR 1.38, 95% CI 1.20 to 1.58) that was not observed among men. An elevated rate of breast cancer was observed among female labourers (HR 1.36, 95% CI 1.01 to 1.82) and moulders (HR 1.47, 95% CI 0.91 to 2.37) in plastics and rubber product fabricating occupations. Overall, elevated rates were observed for oesophageal, liver, stomach, prostate and kidney cancer in job-specific subgroups, including mixing and blending, bonding and cementing, and labouring. There was little evidence of association for lymphatic or haematopoietic cancers. CONCLUSIONS: Findings for lung and breast cancer in women are consistent with other studies and warrant further attention in Ontario. Given the relatively young age at end of follow-up, surveillance in these workers should continue as the cohort ages.
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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 | 0.004 |
| Science and technology studies | 0.002 | 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".