Dairy Foods, Calcium, and Colorectal Cancer: A Pooled Analysis of 10 Cohort Studies
Bibliographic record
Abstract
BACKGROUND: Studies in animals have suggested that calcium may reduce the risk of colorectal cancer. However, results from epidemiologic studies of intake of calcium or dairy foods and colorectal cancer risk have been inconclusive. METHODS: We pooled the primary data from 10 cohort studies in five countries that assessed usual dietary intake by using a validated food frequency questionnaire at baseline. For most studies, follow-up was extended beyond that in the original publication. The studies included 534 536 individuals, among whom 4992 incident cases of colorectal cancer were diagnosed between 6 and 16 years of follow-up. Pooled multivariable relative risks for categories of milk intake and quintiles of calcium intake and 95% confidence intervals (CIs) were calculated. All statistical tests were two-sided. RESULTS: Milk intake was related to a reduced risk of colorectal cancer. Compared with the lowest category of intake (<70 g/day), relative risks of colorectal cancer for increasing categories (70-174, 175-249, and > or =250 g/day) of milk intake were 0.94 (95% CI = 0.86 to 1.02), 0.88 (95% CI = 0.81 to 0.96), and 0.85 (95% CI = 0.78 to 0.94), respectively (P(trend)<.001). Calcium intake was also inversely related to the risk of colorectal cancer. The relative risk for the highest versus the lowest quintile of intake was 0.86 (95% CI = 0.78 to 0.95; P(trend) =.02) for dietary calcium and 0.78 (95% CI = 0.69 to 0.88; P(trend)<.001) for total calcium (combining dietary and supplemental sources). These results were consistent across studies and sex. The inverse association for milk was limited to cancers of the distal colon (P(trend)<.001) and rectum (P(trend) =.02). CONCLUSION: Higher consumption of milk and calcium is associated with a lower risk of colorectal cancer.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.023 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".