Bayesian Space–Time Analysis of Brain Cancer Incidence in Southern Ontario, Canada: 2010–2013
Bibliographic record
Abstract
Canada has one of the highest incidence rates of brain cancer in the world. This study investigates the space-time variation of brain cancer risk across Southern Ontario, Canada. A Bayesian spatio-temporal regression model is used to estimate the relative risk of brain cancer in the 12 spatial health units of Southern Ontario over a four-year period (2010-2013). This work also explores the association between brain cancer and two potential risk factors: traumatic head injury (THI) and excess body fat (EBF). Across all areal units from 2010-2013, results show that the relative risk of brain cancer ranged from 0.83 (95% credible interval (CI) 0.74-0.91) to 1.26 (95% CI 1.13-1.41). Over the years, the eastern and western health units had persistently higher risk levels compared to those in the central areas. Results suggest that areas with elevated THI rates and EBF levels were also potentially associated with higher brain cancer relative risk. Findings revealed that the mean temporal trend for cancer risk progression in the region smoothly decreased over time. Overall, 50% of the health units displayed area-specific trends which were higher than the region's average, thus indicating a slower decrease in cancer rates for these areas in comparison to the mean trend.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".