Association between Known Risk Factors and Colorectal Cancer Risk in Indigenous People Participating in the Ontario Familial Colon Cancer Registry
Bibliographic record
Abstract
Introduction: Colorectal cancer is one of the most common cancers in Ontario and imposes a high burden on many Indigenous populations. There are two aims for this short communication: ■ Highlight colorectal risk factor findings from a population-based case-control study■ Highlight trends and challenges of colorectal cancer research in Indigenous populations in Ontario. Methods: Prevalences of cigarette smoking, obesity, fruit and vegetable consumption, and family history of colorectal cancer were estimated using the Indigenous identifier in the Ontario Familial Colon Cancer Registry for 1999-2007 and then compared for cases and controls using age-adjusted odds ratios (ors) with 95% confidence intervals (cis). Results: The registry search identified 66 Indigenous cases and 23 Indigenous controls. Cigarette smoking (or: 1.88; 95% ci: 0.63 to 5.60) and obesity (or: 2.16; 95% ci: 0.72 to 6.46) were higher in cases, but not statistically significantly so. Conclusions: Findings were consistent with previous literature describing Indigenous populations. A small sample size and poor Indigenous identification questions make it challenging to comprehensively understand cancer risk factors and burden in Indigenous populations.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".