Occupational variation in incidence of bladder cancer: a comparison of population-representative cohorts from Nordic countries and Canada
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to compare occupational variation of the risk of bladder cancer in the Nordic countries and Canada. METHODS: In the Nordic Occupational Cancer study (NOCCA), 73 653 bladder cancer cases were observed during follow-up of 141.6 million person-years. In the Canadian Census Health and Environment Cohort (CanCHEC), 8170 cases were observed during the follow-up of 36.7 million person-years. Standardised incidence ratios with 95% CI were estimated for 53 occupations in the NOCCA cohort and HR with 95% CIs were estimated for 42 occupations in the CanCHEC. RESULTS: Elevated risks of bladder cancer were observed among hairdressers, printers, sales workers, plumbers, painters, miners and laundry workers. Teachers and agricultural workers had reduced risk of bladder cancer in both cohorts. Chimney-sweeps, tobacco workers and waiters had about 1.5-fold risk in the Nordic countries; no risk estimates for these categories were given from the CanCHEC cohort. CONCLUSION: We observed different occupational patterns in risk of bladder cancer in Nordic countries and Canada. The only occupation with similarly increased risk was observed among sales workers. Differences in smoking across occupational groups may explain some, but not all, of this variation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".