Occupational exposure to wood dust and risk of lung cancer in two population-based case–control studies in Montreal, Canada
Bibliographic record
Abstract
BACKGROUND: Wood dust is one of the oldest and one of the most common occupational exposures in the world. The present analyses examine the effect of lifetime exposure to wood dust in diverse occupational settings on lung cancer risk. METHODS: We conducted two population-based case-control studies in Montreal: Study I (1979-1986) included 857 cases and two sets of controls (533 population and 1349 cancer controls), and Study II (1996-2001) comprised 736 cases and 894 population controls. Detailed job histories were obtained by interview and each job was evaluated by expert chemist-hygienists to estimate the likelihood and level of exposure to many substances, one of which was wood dust. Odds ratios (ORs) were computed in relation to different indices of exposure to wood dust, adjusting for several covariates including smoking. Three datasets were analysed: Study I with population controls, Study I with cancer controls, and Study II. RESULTS: The most frequently exposed occupations in our study population were in construction, timber and furniture making industries. We found increased risks of lung cancer for substantial cumulative exposure to wood dust in Study I with cancer controls, (OR = 1.4: 95% confidence interval 1.0;-2.0) and in Study II (OR = 1.7: 95% confidence interval 1.1-2.7). There were no excess risks of lung cancer in any of the three datasets among workers whose cumulative exposure was not substantial. These tendencies held equally within strata of low smokers and heavy smokers. CONCLUSION: There was evidence of increased risk of lung cancer among workers with substantial cumulative exposure to wood dust.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".