P-206 ‘Assessing the Impact of Exposure Control on Future Cancer Burden among Construction Workers’
Bibliographic record
Abstract
<h3>Objectives</h3> Construction workers are exposed to several carcinogens at work. Implementing intervention methods may reduce workers’ exposure, which should subsequently reduce the number of cancer cases attributable to the exposure. The current study estimates the future cancer burden due to several common carcinogens among Ontario construction workers, and assesses the impact of implementing interventions on this burden. This presentation focuses on solar ultraviolet radiation and asbestos. <h3>Methods</h3> The annual number of new cancer cases attributable to each carcinogen was estimated from 2030 to 2060 using Levin’s equation based on the prevalence of exposure (PrE) and the risk of cancer (RR) associated with exposure. The RR was selected from a review of the epidemiologic literature. The PrE was estimated using CAREX Canada’s estimates of prevalence and level of exposure, combined with historical and projected employment data, labour force characteristics, and survival probabilities. The intervention methods specific to each carcinogen were assumed to be fully implemented from 2020, and incorporated into the model by adjusting prevalence and level of exposure downwards. <h3>Results</h3> We estimated that without intervention, 27645 non-melanoma skin cancers would be attributable to sun exposure in Ontario construction workers from 2030 to 2060. Using portable shade structure and hat/long sleeve clothes, a total of 1957 and 2503 of these cases would be prevented, respectively. For asbestos, the two interventions, asbestos ban and building registry, would prevent 56 and 439 lung cancers out of the 6022 attributable cases from 2030 to 2060 if no intervention was applied. <h3>Conclusions</h3> Future work-related cancers can be prevented by reducing workers’ exposure. Combining the economic assessment of both the cancer burden and the costs of implementing exposure controls will help to assess the cost-benefit of different intervention methods, which can be used to direct intervention strategies in construction workplace.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".