Risk of Colon Cancer and Coffee, Tea, and Sugar-Sweetened Soft Drink Intake: Pooled Analysis of Prospective Cohort Studies
Bibliographic record
Abstract
BACKGROUND: The relationships between coffee, tea, and sugar-sweetened carbonated soft drink consumption and colon cancer risk remain unresolved. METHODS: We investigated prospectively the association between coffee, tea, and sugar-sweetened carbonated soft drink consumption and colon cancer risk in a pooled analysis of primary data from 13 cohort studies. Among 731 441 participants followed for up to 6-20 years, 5604 incident colon cancer case patients were identified. Study-specific relative risks (RRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models and then pooled using a random-effects model. All statistical tests were two-sided. RESULTS: Compared with nonconsumers, the pooled multivariable relative risks were 1.07 (95% CI = 0.89 to 1.30, P(trend) = .68) for coffee consumption greater than 1400 g/d (about six 8-oz cups) and 1.28 (95% CI = 1.02 to 1.61, P(trend) = .01) for tea consumption greater than 900 g/d (about four 8-oz cups). For sugar-sweetened carbonated soft drink consumption, the pooled multivariable relative risk comparing consumption greater than 550 g/d (about 18 oz) to nonconsumers was 0.94 (95% CI = 0.66 to 1.32, P(trend) = .91). No statistically significant between-studies heterogeneity was observed for the highest category of each beverage consumed (P > .20). The observed associations did not differ by sex, smoking status, alcohol consumption, body mass index, physical activity, or tumor site (P > .05). CONCLUSIONS: Drinking coffee or sugar-sweetened carbonated soft drinks was not associated with colon cancer risk. However, a modest positive association with higher tea consumption is possible and requires further study.
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.025 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".