Glycemic Load, Carbohydrate Intake, and Risk of Colorectal Cancer in Women: A Prospective Cohort Study
Bibliographic record
Abstract
Mounting evidence suggests that high circulating levels of insulin might be associated with increased colorectal cancer risk. The glycemic effects of diets high in refined starch may increase colorectal cancer risk by affecting insulin and/or insulin-like growth factor-I levels. We examined the association between dietary intake and colorectal cancer risk in a cohort of 49 124 women participating in a randomized, controlled trial of screening for breast cancer in Canada. Linkages to Canadian mortality and cancer databases yielded data on mortality and cancer incidence up to December 31, 2000. During an average 16.5 years of follow-up, we observed 616 incident cases of colorectal cancer (436 colon cancers, 180 rectal cancers). Rate ratios for colorectal cancer for the highest versus the lowest quintile level were 1.05 (95% confidence interval [CI] = 0.73 to 1.53; P(trend) =.94) for glycemic load, 1.01 (95% CI = 0.68 to 1.51; P(trend) =.66) for total carbohydrates, and 1.03 (95% CI = 0.73 to 1.44; P(trend) =.71) for total sugar. Our data do not support the hypothesis that diets high in glycemic load, carbohydrates, or sugar increase colorectal cancer risk.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".