Occupational exposure to chemical and petrochemical industries and bladder cancer risk in four western Canadian provinces.
Bibliographic record
Abstract
Occupational factors have been proposed to play a critical role in bladder cancer. This population-based case-control study was conducted to confirm the association between selected occupational and non-occupational risk factors and risk of bladder cancer using data collected from the four western Canadian provinces. Unconditional logistic regression analyses were based on 549 histologically confirmed bladder cancer cases and 1099 controls. Bladder cancer risk was found to increase with increasing pack-years of cigarette smoking with an odds ratio (OR) in the highest quartile of 3.32 (95% confidence interval [CI], 2.28-4.82). A dose-response relationship was demonstrated between bladder cancer and pack-years of smoking (p < 0.0001). A positive trend was observed with coffee consumption in men (p < 0.0001), with the highest risk in the highest category of exposure: drinkers of four cups or more per day had an OR of 1.77 (95% CI 1.11-2.82). Increased bladder cancer risk was associated with self-reported exposure at work to several chemicals: asbestos (OR 1.69 [95% CI 1.07-2.65]); mineral, cutting or lubricating oil (1.64 [95% CI 1.06-2.55]); benzidine (2.20 [95% CI 1.00-4.87]). The population attributable fraction (PAF) estimates were 51% for cigarette smoking, 17% for heavy coffee consumption, 10% for mineral, cutting or lubricating oil exposure, 6% for asbestos exposure, and 1% for benzidine exposure. Although self-reported chemical exposures have important limitations, the findings are suggestive of increased risk for several associations previously reported between chemical agents or industries and risk of bladder cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".