Dietary flavonoid intake and colorectal cancer: a case–control study
Bibliographic record
Abstract
Diets rich in flavonoids may reduce the risk of developing colorectal cancer. Flavonoids are widely distributed in foods of plant origin, though in the UK tea is the main dietary source. Our objective was to evaluate any independent associations of total dietary and non-tea intake of four flavonoid subclasses and the risk of developing colorectal cancer in a tea-drinking population with a high colorectal cancer incidence. A population-based case-control study (264 cases with histologically confirmed incident colorectal cancer and 408 controls) was carried out. Dietary data gathered by FFQ were used to calculate flavonoid intake. Adjusted OR and 95 % CI were estimated by logistic regression. No linear association between risk of developing colorectal cancer and total dietary flavonol, procyanidin, flavon-3-ol or flavanone intakes was found, but non-tea flavonol intake was inversely associated with colorectal cancer risk (OR 0.6; 95 % CI 0.4, 1.0). Stratification by site of cancer and assessment of individual flavonols showed a reduced risk of developing colon but not rectal cancer with increasing non-tea quercetin intake (OR 0.5; 95 % CI 0.3, 0.8; P(trend) < 0.01). We concluded that flavonols, specifically quercetin, obtained from non-tea components of the diet may be linked with reduced risk of developing colon cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".