Reported intake of selected micronutrients and risk of colorectal cancer: results from a large population-based case-control study in Newfoundland, Labrador and Ontario, Canada.
Bibliographic record
Abstract
AIM: The impact of micronutrient intake and colorectal cancer (CRC) risk is poorly understood. The objective of this study was to evaluate the associations of selected micronutrients with risk of incident CRC in study participants from Newfoundland, Labrador (NL) and Ontario (ON), Canada. MATERIALS AND METHODS: We conducted a population-based study among 1760 case participants and 2481 age- and sex-matched control participants. Information on diet and other lifestyle factors were measured using a food frequency questionnaire and a personal history questionnaire. Odds ratios (OR) and 95% confidence intervals (CI) were calculated using unconditional logistic regression, controlling for covariables. RESULTS: Highest compared to lowest quartile intakes of certain micronutrients were associated with lower risk of CRC, including: calcium (from food and supplements (FS), OR=0.59; 95% CI=0.45-0.77, and from food only (FO): OR=0.76, 95% CI=0.59-0.97), vitamin C (FS:OR=0.67; 95%CI:0.51-0.88), vitamin D (FS: OR=0.73; 95% CI: 0.57-0.94, FO: OR=0.79, 95% CI=0.62-1.00), riboflavin (FS: OR=0.61; 95% CI=0.47-0.78, and folate (FS: OR=0.72; 95% CI=0.56-0.92). Higher risk of CRC was observed for iron intake (highest versus lowest quintiles: OR=1.34, 95% CI=1.01-1.78). CONCLUSION: This study presents evidence that dietary intake of calcium, vitamin D, vitamin C, riboflavin and folate are associated with a lower risk of incident CRC and that dietary intake of iron may be associated with a higher risk of the disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".