Fruit and vegetable consumption and lung cancer risk: Updated information from the European Prospective Investigation into Cancer and Nutrition (EPIC)
Bibliographic record
Abstract
The association of fruit and vegetable consumption and lung cancer incidence was evaluated using the most recent data from the European Prospective Investigation into Cancer and Nutrition (EPIC), applying a refined statistical approach (calibration) to account for measurement error potentially introduced by using food frequency questionnaire data. Between 1992 and 2000, detailed information on diet and life-style of 478,590 individuals participating in EPIC was collected. During a median follow-up of 6.4 years, 1,126 lung cancer cases were observed. Multivariate Cox proportional hazard models were applied for statistical evaluation. In the whole study population, fruit consumption was significantly inversely associated with lung cancer risk while no association was found for vegetable consumption. In current smokers, however, lung cancer risk significantly decreased with higher vegetable consumption; this association became more pronounced after calibration, the hazard ratio (HR) being 0.78 (95% CI 0.62-0.98) per 100 g increase in daily vegetable consumption. In comparison, the HR per 100 g fruit was 0.92 (0.85-0.99) in the entire cohort and 0.90 (0.81-0.99) in smokers. Exclusion of cases diagnosed during the first 2 years of follow-up strengthened these associations, the HR being 0.71 (0.55-0.94) for vegetables (smokers) and 0.86 (0.78-0.95) for fruit (entire cohort). Cancer incidence decreased with higher consumption of apples and pears (entire cohort) as well as root vegetables (smokers). In addition to an overall inverse association with fruit intake, the results of this evaluation add evidence for a significant inverse association of vegetable consumption and lung cancer incidence in smokers.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".