The North–South and East–West Gradient in Colorectal Cancer Risk: A Look at the Distribution of Modifiable Risk Factors and Incidence across Canada
Bibliographic record
Abstract
Colorectal cancer (crc) is the 2nd most common cancer in Canada and the 2nd leading cause of cancer death. That heavy burden can be mitigated given the preventability of crc through lifestyle changes and screening. Here, we describe the extent of the variation in crc incidence rates across Canada and the disparities, by jurisdiction, in the prevalence of modifiable risk factors known to contribute to the crc burden. Findings suggest that there is a north-south and east-west gradient in crc modifiable risk factors, including excess weight, physical inactivity, excessive alcohol consumption, and low fruit and vegetable consumption, with the highest prevalence of risk factors typically found in the territories and Atlantic provinces. In general, that pattern reflects the crc incidence rates seen across Canada. Given the substantial interjurisdictional variation, more work is needed to increase prevention efforts, including promoting a healthier diet and lifestyle, especially in jurisdictions facing disproportionately higher burdens of crc. Based on current knowledge, the most effective approaches to reduce the burden of crc include adopting public policies that create healthier environments in which people live, work, learn, and play; making healthy choices easier; and continuing to emphasize screening and early detection. Strategic approaches to modifiable risk factors and mechanisms for early cancer detection have the potential to translate into positive effects for population health and fewer Canadians developing and dying from 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".