Spatial Patterns and Hot Spot Analysis of Primary Malignant Brain and Central Nervous System Cancers in Canada from 2016 to 2020
Bibliographic record
Abstract
Primary malignant brain and central nervous system cancers are among the most lethal globally, yet their causes remain poorly understood. This thesis presents a spatial epidemiological analysis of these cancers diagnosed in Canada (2016-2020). Using data from the Canadian Cancer Registry, incidence rates were calculated and mapped at provincial and small-area levels. Statistical methods, including disease mapping and spatial cluster detection, were used to identify high-incidence rate regions. Negative binomial regression assessed associations between incidence rates in Ontario and proximity to environmental exposures. The analysis revealed geographic disparities, with the highest rates in the Atlantic provinces and clusters near the Great Lakes. Incidence rates were higher among males and older individuals. A significant association was observed with proximity to seaplane bases. These findings provide detailed spatial insights into the distribution of these cancers in Canada, highlighting the need for further research on environmental exposures and advanced spatial modeling.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".