Surveillance of asbestos related disease among workers enrolled in an exposure registry
Bibliographic record
Abstract
INTRODUCTION: Contemporary asbestos exposure occurs during construction, remediation, and maintenance involving asbestos-containing materials (ACM), as compared to the historical exposure scenarios of asbestos mining and milling. The Ontario Asbestos Workers Register (AWR) was established in 1986 to track asbestos exposure among construction workers. This study reports on the risk of asbestos-related diseases (ARD) among workers in the AWR. METHODS: AWR registrants were linked probabilistically with administrative health databases (1986-2019) to identify cases of ARD including both cancer and chronic respiratory disease. Follow-up began at AWR enrollment and continued prospectively. Incidence rates were compared to the general population using standardized incidence ratios (SIRs). Associations between ACM exposure and ARD were estimated among AWR registrants using Poisson regression. RESULTS: In total, 26,204 (81%) registrants were linked successfully. Common industries of employment were construction (62%), manufacturing (19%) and education (8%). Among men and women mesothelioma (M:SIR 6.83 [95% CI = 5.56-8.31]; W:SIR 19.2 [3.86-56.1]) and pulmonary fibrosis (M:SIR 14.1 [12.2-16.2]; W:SIR 9.25 [2.49-23.7]) rates were higher than the general population. Asbestosis risk was elevated among men (M:SIR 11.2 [9.59-13.1]). Workers with longer reported exposures (≥140 h) had increased rates of lung cancer (RR 1.34 [1.10-1.63]), mesothelioma (RR 2.83 [1.75-4.58]), asbestosis (RR 3.07 [2.12-4.43]), chronic obstructive pulmonary disease (RR 1.42 [1.29-1.57]), and pulmonary fibrosis (RR 1.88 [1.35-2.62]). CONCLUSION: Exposure to asbestos in construction and building maintenance continues to contribute to ARD incidence. Despite a Canadian ban on asbestos in new products, exposures to existing ACM will persist from construction activities. The AWR offers an opportunity for ongoing surveillance of resulting ARD in Ontario.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".