8286874 Impact of interventions to prevent asbestos-related respiratory disease in an exposed worker registry using a simplified G-computation
Bibliographic record
Abstract
Background 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 2,000 reported hours of work with asbestos. However, recent research showed a substantial excess of asbestos-related respiratory disease among workers in the registry indicating that the notification policy requires revision. We sought to evaluate the impact on disease prevention of alternative policies limiting asbestos work hours among registry participants using a novel application of parametric G-computation we term ‘G-POSH’. Methods A cohort of 26,164 asbestos registry workers were followed for cancer and non-malignant disease diagnoses in Ontario from 1986 through 2019. G-POSH was used to estimate decreases in disease risk under five hypothetical exposure reduction interventions. Inverse probability of censoring weighting was used to adjust for time-varying confounding due to healthy worker survivor bias. Results Standard Poisson regression analyses of the association between cumulative asbestos work hours and respiratory disease incidence rates showed substantially elevated rates well before reaching 2,000 asbestos work hours. Using G-POSH, limiting cumulative asbestos work hours to 100 hours would have reduced the risk of asbestosis by half (Risk Ratio, RR: 0.52, 95% CI 0.43-0.63) and lung cancer by 15% (RR: 0.85, 95% CI 0.78-0.92) compared to the observed natural course in the cohort. Limiting exposure to 2,000 asbestos work hours had a smaller but still substantial impact on prevention of asbestosis (RR 0.77, 95% CI: 0.70-0.86). Inverse probability weighted estimates showed a minor influence of healthy worker bias. Conclusion G-POSH is a simplified tool for estimating intervention effects in occupational cohorts. 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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".