Male bladder cancer risk and occupational exposure according to a job-exposure matrix—a case-control study in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: The authors investigated the risk of bladder cancer in association with exposure to over 12 000 occupational chemical agents, complex mixtures, and other substances (hereafter referred to as chemical agents). METHODS: Adult males diagnosed with cancer between 1983 and 1990 in British Columbia, Canada, were surveyed. Detailed occupational histories and confounding information was provided by a self-administered questionnaire. Cancer controls were matched to bladder cancer cases, resulting in 1062 cases and 8057 controls for the analysis. An extensive United-States-based job-exposure matrix was applied to estimate cumulative exposure to occupational chemical agents. Odds ratios for bladder cancer due to exposure to chemical agents were estimated via conditional logistic regression analyses, adjusted for important confounders. RESULTS: A significantly (P<0.05) increased risk was detected for ever exposure to 635 chemical agents, and 341 chemical agents exhibited a significantly increasing dose-response relationship. Adjustment for multiple comparisons resulted in a subset of 29 chemical agents that continued to show significant results. A principal components analysis classified these 29 chemical agents into five independent groups, distinguished mainly by job. Exposures to these chemical agents were largely due to employment in the logging and construction industries and occupations involving motor vehicles. Consistent results were observed for bladder carcinogens reported in the literature. CONCLUSIONS: This study suggests that several specific chemical agents were significantly associated with the risk of bladder cancer. The chemical agents were mainly derivatives or combustion products of fossil fuels. The results corroborate important findings from the literature and document a risk for specific chemical agents not previously reported.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".