8291409 Work and heart disease: acute myocardial infarction mortality in a cohort of over two million Ontario, Canada workers
Bibliographic record
Abstract
Background Despite heart disease being the second leading cause of death in Canada, the role of occupation in acute myocardial infarction (AMI) risk remains poorly understood. We examined occupational patterns in AMI mortality among workers in Ontario, Canada, and compared to previously observed AMI incidence within the same population. Methods Workers were identified through the Occupational Disease Surveillance System, linking accepted lost-time workers’ compensation claims from the Workplace Safety and Insurance Board to mortality data (2009–2021) for 2.37 million workers. Individuals aged 15 to 85 were followed to identify deaths due to AMI. Adjusted cox proportional hazard models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for AMI mortality by occupation. Results A total of 8,996 AMI-related deaths were identified. Mortality patterns mirrored some findings in incidence, with similar risks among nursing aides and orderlies (HR=1.11, 95% CI=0.93–1.32); guards and watchmen/women (HR=1.42, 95% CI=1.18–1.70); metal processing (HR=1.40, 95% CI=1.17–1.66); excavating, grading, and paving (HR=1.33, 95% CI=1.10–1.62); and truck driving (HR=1.46, 95% CI=1.35–1.57). However, some occupations showed elevated mortality risk only, such as moulding and metal casting (HR=1.48, 95% CI=1.04–2.11); clay, glass, and stone processing (HR=1.40, 95% CI=1.03–1.89); wood machining (HR=1.60, 95% CI=1.10–2.34); paper product fabricating/assembling (HR=1.59, 95% CI=1.12–2.25); railway track work (HR=2.09, 95% CI=1.28–3.41); motor transport foremen/women (HR=2.01, 95% CI=1.23-3.28) and mining foremen/women (HR=1.73, 95% CI=0.93–3.22). Conclusion Variations in AMI mortality and incidence may reflect differences in healthcare access, biological factors, occupational physical demands, and exposures such as noise, vibration, shift work, and chemical hazards. The healthy worker effect may also contribute to observed patterns.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.003 | 0.001 |
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".