Wood dust exposure and risk of lung cancer
Bibliographic record
Abstract
OBJECTIVES: Despite the compelling association between wood dust and sinonasal cancer, there has been little systematic and rigorous study of the relationship between wood dust and lung cancer. We investigated whether a history of wood dust exposure through occupational and hobby-related activities was associated with increased lung cancer risk. METHODS: We conducted a population-based case-control study, with 440 cases and 845 age-matched controls. Using detailed work and personal histories, quantitative estimates of cumulative exposure to wood dust (thought to be primarily from softwood) were calculated for each participant. Using unconditional logistic regression adjusted for age and smoking status, risk of lung cancer was examined in relation to employment in wood-related occupations, working with wood as a hobby, as well as cumulative wood dust exposure that took into account both occupational and hobby-related sources. RESULTS: While we observed an increased risk of lung cancer associated with working in a sawmill (OR=1.5; 95% CI: 1.1, 2.1), we found no evidence of increased risks with other occupations, working with wood as a hobby or with estimated cumulative exposure to wood dust. Contrary to our hypothesis, we observed modest decreased risks with exposure to wood dust, although no dose-response relationship was apparent. CONCLUSIONS: This study provided somewhat reassuring evidence that softwood dust does not increase the risk of lung cancer, but future studies should evaluate exposure to hardwood dusts. Suggestive evidence for an inverse association may be attributable to the presence of endotoxin in the wood dust, but the lack of a dose-response relationship suggests a non-causal relationship.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".