Investigating the relationship between modifiable environmental risk factors and incidence of colorectal cancer: a community based study
Bibliographic record
Abstract
Colorectal cancer is the third most diagnosed cancer and second leading cause of cancer related deaths in Canada. As Ontario has the largest population in Canada, it also has great disparities in colorectal cancer incidence. The region of Timiskaming has the highest incidence for colorectal cancer, while the region of Peel has the lowest incidence for colorectal cancer in Ontario. The purpose of this study is to identify the dominant non-nutritional modifiable environmental risk factors in the region of Timiskaming compared to the region of Peel that may be associated with diverging colorectal cancer incidence rates. The three objectives of the study included performing a systematic review on available published literature, creating an assessment questionnaire tool regarding environmental exposures, and utilizing the questionnaire assessment tool within a pilot study group while expanding it into the communities of interest. Findings indicate that there are dominant non-nutritional modifiable environmental risk factors in the regions of Timiskaming and Peel that may be associated with colorectal cancer. The dominant factors identified are tobacco/smoking, alcohol use, pesticides/organochlorines, and metal toxins. Following this study, it is imperative that recommendations are directed at a community level and relate to the assessment of potential non-nutritional modifiable environmental risk factors. Future research should accompany a larger sample size, multiple participant communities, and catering of the questionnaire tool towards the communities of interest.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".