Fruits, vegetables and lung cancer: A pooled analysis of cohort studies
Why this work is in the frame
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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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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 it