Intakes of vitamins A, C and E and folate and multivitamins and lung cancer: A pooled analysis of 8 prospective studies
Bibliographic record
Abstract
Intakes of vitamins A, C and E and folate have been hypothesized to reduce lung cancer risk. We examined these associations in a pooled analysis of the primary data from 8 prospective studies from North America and Europe. Baseline vitamin intake was assessed using a validated food-frequency questionnaire, in each study. We calculated study-specific associations and pooled them using a random-effects model. During follow-up of 430,281 persons over a maximum of 6-16 years in the studies, 3,206 incident lung cancer cases were documented. Vitamin intakes were inversely associated with lung cancer risk in age-adjusted analyses; the associations were greatly attenuated after adjusting for smoking and other risk factors for lung cancer. The pooled multivariate relative risks, comparing the highest vs. lowest quintile of intake from food-only, were 0.96 (95% confidence interval (CI) 0.83-1.11) for vitamin A, 0.80 (95% CI 0.71-0.91) for vitamin C, 0.86 (95% CI 0.76-0.99) for vitamin E and 0.88 (95% CI 0.74-1.04) for folate. The association with vitamin C was not independent of our previously reported inverse association with beta-cryptoxanthin. Further, vitamin intakes from foods plus supplements were not associated with a reduced risk of lung cancer in multivariate analyses, and use of multivitamins and specific vitamin supplements was not significantly associated with lung cancer risk. The results generally did not differ across studies or by sex, smoking habits and lung cancer cell type. In conclusion, these data do not support the hypothesis that intakes of vitamins A, C and E and folate reduce 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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.021 |
| Bibliometrics | 0.006 | 0.006 |
| 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".