Fruits, vegetables and lung cancer: A pooled analysis of cohort studies
Bibliographic record
Abstract
Inverse associations between fruit and vegetable consumption and lung cancer risk have been consistently reported. However, identifying the specific fruits and vegetables associated with lung cancer is difficult because the food groups and foods evaluated have varied across studies. We analyzed fruit and vegetable groups using standardized exposure and covariate definitions in 8 prospective studies. We combined study-specific relative risks (RRs) using a random effects model. In the pooled database, 3,206 incident lung cancer cases occurred among 430,281 women and men followed for up to 6-16 years across studies. Controlling for smoking habits and other lung cancer risk factors, a 16-23% reduction in lung cancer risk was observed for quintiles 2 through 5 vs. the lowest quintile of consumption for total fruits (RR = 0.77; 95% CI = 0.67-0.87 for quintile 5; p-value, test for trend < 0.001) and for total fruits and vegetables (RR = 0.79; 95% CI = 0.69-0.90; p-value, test for trend = 0.001). For the same comparison, the association was weaker for total vegetable consumption (RR = 0.88; 95% CI = 0.78-1.00; p-value, test for trend = 0.12). Associations were similar between never, past, and current smokers. These results suggest that elevated fruit and vegetable consumption is associated with a modest reduction in lung cancer risk, which is mostly attributable to fruit, not vegetable, intake. However, we cannot rule out the possibility that our results are due to residual confounding by smoking. The primary focus for reducing lung cancer incidence should continue to be smoking prevention and cessation.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 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".