Dietary Carotenoids and Risk of Colorectal Cancer in a Pooled Analysis of 11 Cohort Studies
Bibliographic record
Abstract
Dietary carotenoids have been hypothesized to protect against epithelial cancers. The authors analyzed the associations between intakes of specific carotenoids (alpha-carotene, beta-carotene, beta-cryptoxanthin, lutein + zeaxanthin, and lycopene) and risk of colorectal cancer using the primary data from 11 cohort studies carried out in North America and Europe. Carotenoid intakes were estimated from food frequency questionnaires administered at baseline in each study. During 6-20 years of follow-up between 1980 and 2003, 7,885 incident cases of colorectal cancer were diagnosed among 702,647 participants. The authors calculated study-specific multivariate relative risks and then combined them using a random-effects model. In general, intakes of specific carotenoids were not associated with colorectal cancer risk. The pooled multivariate relative risks of colorectal cancer comparing the highest quintile of intake with the lowest ranged from 0.92 for lutein + zeaxanthin to 1.04 for lycopene; only for lutein + zeaxanthin intake was the result borderline statistically significant (95% confidence interval: 0.84, 1.00). The associations observed were generally similar across studies, for both sexes, and for colon cancer and rectal cancer. These pooled data did not suggest that carotenoids play an important role in the etiology 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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".