Consumption of nuts and seeds and pancreatic ductal adenocarcinoma risk in the European Prospective Investigation into Cancer and Nutrition
Bibliographic record
Abstract
Four epidemiologic studies have assessed the association between nut intake and pancreatic cancer risk with contradictory results. The present study aims to investigate the relation between nut intake (including seeds) and pancreatic ductal adenocarcinoma (PDAC) risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Cox proportional hazards models were used to estimate hazards ratio (HR) and 95% confidence intervals (95% CI) for nut intake and PDAC risk. Information on intake of nuts was obtained from the EPIC country-specific dietary questionnaires. After a mean follow-up of 14 years, 476,160 participants were eligible for the present study and included 1,283 PDAC cases. No association was observed between consumption of nuts and PDAC risk (highest intake vs nonconsumers: HR, 0.89; 95% CI, 0.72-1.10; p-trend = 0.70). Furthermore, no evidence for effect-measure modification was observed when different subgroups were analyzed. Overall, in EPIC, the highest intake of nuts was not statistically significantly associated with PDAC 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".