But other than Mesothelioma? An Estimate of the Proportion of Work-Related Cancers in Quebec
Bibliographic record
Abstract
BACKGROUND: More than 30 exposures in the workplace are proven carcinogens. In the present study, we aimed to estimate the burden of occupational cancer in Quebec so as to increase awareness among stakeholders and to prioritize research activities. METHODS: Work-attributable fractions-that is, the proportions of cancers attributable to work-as published in Finland and the United Kingdom were applied to Quebec 2002-2006 cancer incidence and mortality data to estimate the number of work-related cases for 28 cancer sites. RESULTS: Overall, 6.0% of incident cancers (men: 9.1%; women: 2.7%) and 7.6% of cancer deaths (men: 11.8%; women: 2.8%) could be attributable to work, resulting annually in an average of 2160 new cancer diagnoses and 1190 cancer deaths in Quebec. Incident cancers of the lung, prostate, skin, bladder, and (female) breast were the most numerous; cancer sites resulting in more deaths were lung, (female) breast, and pleura. During the same period, compensation statistics reported annual averages of 94.3 incident cancers and 61.9 cancer deaths, mostly involving mesothelioma (64% of compensated incident cancers) and lung cancer (30% of compensated incident cancers). CONCLUSIONS: Increased recognition of workplace cancers by all stakeholders, from workers and employers to treating physicians, will foster appropriate preventive measures for safer workplaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".