Intake of Fruits and Vegetables and Risk of Pancreatic Cancer in a Pooled Analysis of 14 Cohort Studies
Bibliographic record
Abstract
Fruit and vegetable intake may protect against pancreatic cancer, since fruits and vegetables are rich in potentially cancer-preventive nutrients. Most case-control studies have found inverse associations between fruit and vegetable intake and pancreatic cancer risk, although bias due to reporting error cannot be ruled out. In most prospective studies, inverse associations have been weaker and imprecise because of small numbers of cases. The authors examined fruit and vegetable intake in relation to pancreatic cancer risk in a pooled analysis of 14 prospective studies from North America, Europe, and Australia (study periods between 1980 and 2005). Relative risks and 2-sided 95% confidence intervals were estimated separately for the 14 studies using the Cox proportional hazards model and were then pooled using a random-effects model. Of 862,584 men and women followed for 7-20 years, 2,212 developed pancreatic cancer. The pooled multivariate relative risks of pancreatic cancer per 100-g/day increase in intake were 1.01 (95% confidence interval (CI): 0.99, 1.03) for total fruits and vegetables, 1.01 (95% CI: 0.99, 1.03) for total fruits, and 1.02 (95% CI: 0.99, 1.06) for total vegetables. Associations were similar for men and women separately and across studies. These results suggest that fruit and vegetable intake during adulthood is not associated with a reduced pancreatic cancer risk.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 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".