Calcium, Dairy Foods, Vitamin D, and Colorectal Cancer Risk: The Fukuoka Colorectal Cancer Study
Bibliographic record
Abstract
Epidemiologic evidence supporting a protective role of calcium and vitamin D in colorectal carcinogenesis has been accumulating in Western populations, but it is limited in Asian populations, whose intake of calcium is relatively low. We investigated the association of intakes of these nutrients with colorectal cancer risk in Japanese. Study subjects were participants of a large-scale case-control study in Fukuoka, Japan. Diet was assessed through interview regarding 148 dietary items by showing typical foods or dishes on the display of a personal computer. In a multivariate analysis adjusting for potential confounding variables, calcium intake was significantly, inversely associated with colorectal cancer risk (P for trend=0.01); the odds ratio for the highest versus lowest quintile of calcium intake was 0.64 (95% confidence interval, 0.45-0.93). Higher levels of dietary vitamin D were significantly associated with decreased risk of colorectal cancer among those who had fewer chances of sunlight exposure at work or in leisure (P for trend=0.02). A decreased risk of colorectal cancer associated with high calcium intake was observed among those who had higher levels of vitamin D intake or among those who had a greater chance of daily sunlight exposure, but not among those with medium or lower intake of vitamin D or among those with potentially decreased sunlight exposure. These results add to support for a joint action of calcium and vitamin D in the prevention of colorectal carcinogenesis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".