The Risk of Lung Cancer Related to Dietary Intake of Flavonoids
Bibliographic record
Abstract
It has been hypothesized that flavonoids in foods and beverages may reduce cancer risk through antioxidation, inhibition of inflammation, and other antimutagenic and antiproliferative properties. We examined associations between intake of 5 flavonoid subclasses (anthocyanidins, flavan-3-ols, flavones, flavonols, and flavanones) and lung cancer risk in a population-based case-control study in Montreal, Canada (1061 cases and 1425 controls). Flavonoid intake was estimated from a food frequency questionnaire that assessed diet 2 yr prior to diagnosis (cases) or interview (controls). Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using unconditional logistic regression. Overall, total flavonoid intake was not associated with lung cancer risk, the effect being similar regardless of sex and smoking level. However, low flavonoid intake from food, but not from beverages, was associated with an increased risk. The adjusted ORs (95% CIs) comparing the highest vs. the lowest quartiles of intake were 0.63 (0.47-0.85) for total flavonoids, 0.82 (0.61-1.11) for anthocyanidins, 0.67 (0.50-0.90) for flavan-3-ols, 0.68 (0.50-0.93) for flavones, 0.62 (0.45-0.84) for flavonols, and 0.70 (0.53-0.94) for flavanones. An inverse association with total flavone and flavanone intake was observed for squamous cell carcinoma but not adenocarcinoma. In conclusion, low flavonoid intake from food may increase lung cancer risk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".