Workplace exposure to asbestos and the risk of kidney cancer in Canadian men
Bibliographic record
Abstract
OBJECTIVE: Previous studies considered the role of occupational causes in kidney cancer but were limited by small sample sizes and imprecise exposure assessment. This study examined the relationship between occupational exposure to asbestos and the risk of kidney cancer across a range of jobs in a large, population-based case-control study in Canada. METHODS: Data were from the case-control component of the National Enhanced Cancer Surveillance System, a study conducted between 1994 and 1997 in eight Canadian provinces. Male kidney cancer cases, histologically confirmed, and controls completed questionnaires on socio-demographics, anthropometry, diet, smoking, secondhand smoke exposure, and physical activity. Occupational histories were also collected, including each job held for at least 1 year since the age of 18. Occupational hygienists, blinded to case status, assigned exposure to asbestos, considering intensity, frequency, and probability of exposure (each 3-point scales). Logistic regression was used to estimate the odds of kidney cancer in exposed participants (defined using three metrics) compared to those without asbestos exposure. RESULTS: There were 712 cases and 2454 controls in these analyses. Ever-exposure to asbestos was associated with 20% increased odds of kidney cancer compared to unexposed workers (OR 1.2, 95% confidence interval 1.0-1.4 when including possibly exposed workers). A small increase in risk was observed with cumulative exposure, while increasing intensity of exposure was related to increased odds of kidney cancer. CONCLUSIONS: This study found some evidence for an association between occupational exposure to asbestos and kidney cancer. Higher intensity of exposure to asbestos had the strongest relationship with kidney cancer risk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".