Carcinogens exposure prevalence as a first step toward preventing occupational cancers: challenges and drawbacks
Bibliographic record
Abstract
Objectives Ascertainment of the occurrence of exposure to carcinogens and its quantification are the first steps toward preventing occupational cancer. A descriptive study was set up to estimate the importance of potential exposure to carcinogens among the Quebec 3,5 million workers. Methods For agents considered carcinogenic or probably carcinogenic to humans in the Quebec legislation and in the IARC classification (groups 1 and 2A), we obtained estimates of potential exposures by industrial sector from 5 sources: laboratory analyses of chemicals monitored in Quebec workplaces by public health agencies, the 1998 Quebec Social and Health Population Survey, a few specific industrial hygiene research projects, data produced by CAREX Canada (the University of British Columbia equivalent of CAREX), and published data from the French SUMER survey and from the MATGÉNÉ job-exposure matrices project. Labour force data were extracted from the 2006 Canadian Census. Results Potential exposure data were obtained for 19 recognised and 19 suspected carcinogens. Using CAREX Canada data (available for the largest number of carcinogens), the most common exposures were solar radiation (5.7%), diesel exhaust fumes (4.4% of workers), polycyclic aromatic hydrocarbons (2.0%) and wood dust (1.8%). Occasionally, different estimates were obtained from other sources of data; for example, the 1998 Quebec population survey produced a larger estimate for wood dust exposure (10.7%). Conclusions Variations between estimates obtained with different data sources pose a challenge for calculation of the exposed population. However, targeting key population groups for intervention and identification of research priorities and knowledge gaps remain feasible with these data.
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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 | 0.103 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".