Impact of interventions to prevent asbestos-related respiratory disease in an exposed worker registry using a simplified G-computation
Bibliographic record
Abstract
The Ontario Asbestos Workers Registry is a regulatory exposure registry obligating employers to report the number of work hours with asbestos-containing materials for each of their workers. Currently, each worker is notified of the need for a medical examination once they have accrued 2000 reported hours of work with asbestos. We sought to evaluate the impact on disease prevention of alternative policies limiting asbestos work hours among registry participants. A cohort of 26 164 asbestos workers were followed for cancer and nonmalignant disease diagnoses between 1986 and 2019. Analyses of the association between cumulative asbestos work hours and respiratory disease incidence rates showed substantially elevated disease rates well before reaching 2000 asbestos work hours. Using a simplified application of parametric G-computation (G-POSH), limiting cumulative asbestos work hours to 100 h would have prevented 76 asbestosis, 36 pulmonary fibrosis, 27 mesothelioma, and 79 lung cancer cases at the end of follow-up compared to the observed risk in the cohort. Limiting exposure to 2000 asbestos work hours had a smaller but still substantial impact on disease prevention, particularly among workers in the construction industry. Regulatory agencies should intervene sooner to prevent respiratory disease among workers in the registry.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".