Inverse Association between Dietary Intake of Selected Carotenoids and Vitamin C and Risk of Lung Cancer
Bibliographic record
Abstract
While diets rich in fruit and vegetables appear to reduce lung cancer risk, the evidence for individual carotenoid and vitamin intakes has been judged too limited to reach firm conclusions. Data from a case-control study of lung cancer (Montreal, QC, Canada, 1996-2002) were used to investigate the role of dietary intakes of β-carotene, α-carotene, β-cryptoxanthin, lutein/zeaxanthin, lycopene, and vitamin C in lung cancer risk. In-person interviews elicited dietary information from 1,105 incident cases and 1,449 population controls. Usual frequency of consumption of 49 fruits and vegetables 2 years prior to diagnosis/interview was collected. Odds ratios (ORs) and 95% confidence intervals (CIs) between intake variables and lung cancer were estimated using logistic or polytomous regression, adjusting for potential confounding factors including a detailed smoking history. ORs associated with upper versus lower tertiles of intake were 0.66 (95% CI = 0.51-0.84) for β-carotene, 0.70 (95% CI = 0.55-0.90) for α-carotene, 0.65 (95% CI = 0.51-0.84) for β-cryptoxanthin, 0.75 (95% CI = 0.59-0.95) for lycopene, and 0.74 (95% CI = 0.58-0.96) for vitamin C. ORs suggestive of a protective effect were found for elevated intakes of β-carotene, α-carotene, β-cryptoxanthin, and lycopene in male heavy smokers and of vitamin C in female heavy smokers. Selected antioxidants were also associated with a lower risk of lung cancer in female moderate smokers, and of squamous cell carcinoma, adenocarcinoma, and small cell carcinoma. These results suggest that several dietary antioxidants found in common food sources may protect against lung cancer, even among heavy smokers.
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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 | 0.006 |
| 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 | 0.001 |
| 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".