Assessing geographic and industry-related trends in bladder cancer in Ontario: A population-based study
Bibliographic record
Abstract
INTRODUCTION: Bladder cancer (BC) is the fifth most prevalent cancer in Canada, with 9000 Canadians diagnosed each year. While smoking is the most important risk factor, environmental and occupational carcinogens have been found to significantly contribute to BC rates. As Canada is highly reliant on natural resource industries, this study seeks to identify geographical and industry-related trends of BC rates in Ontario. METHODS: The 1991 and 2001 Canadian Census Health and Environment Cohort (CanCHEC; Statistics Canada) was used, along with individual years of census data. Maps identifying hot and cold spots for BC within Ontario were generated, and the former were assessed for industry patterns between location and BC rates. Cox proportional hazards models were run for each age cohort to predict the likelihood of developing BC by industry of work. RESULTS: Significant geographical and industrial trends in BC rates were identified. For 1991-2001, hot spots included the Cochrane, Manitoulin, Parry Sound, and Sudbury (90% confidence interval [CI]), and Nipissing and Temiskaming (95% CI) regions. Toronto and York were cold spots. Concurrently, metal (p=0.039), paper and publishing (p=0.0062), and wood and furniture (p<0.0001) industries had increased rates of BC. Notably, these industries had high employment density in our hot spot areas and low density in our cold spots. CONCLUSIONS: Significant geographical and industrial BC trends were found in Northern Ontario regions reliant on heavy employment in natural resource-based industries, such as forestry, agriculture, and wood/paper. These findings may inform future screening guidelines and aid in identifying individuals at risk of BC development.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.002 | 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".