Consumption of fish and meats and risk of hepatocellular carcinoma: the European Prospective Investigation into Cancer and Nutrition (EPIC)
Bibliographic record
Abstract
BACKGROUND: While higher intake of fish and lower consumption of red/processed meats have been suggested to play a protective role in the etiology of several cancers, prospective evidence for hepatocellular carcinoma (HCC) is limited, particularly in Western European populations. METHODS: The associations of fish and meats with HCC risk were analyzed in the EPIC cohort. Between 1992 and 2010, 191 incident HCC were identified among 477 206 participants. Baseline diet was assessed using validated dietary questionnaires. A single 24-h diet recall from a cohort subsample was used for calibration. Multivariable proportional hazard regression was utilized to estimate hazard ratios (HR) and 95% confidence intervals (CI). In a nested case-control subset (HCC = 122), HBV/HCV status and liver function biomarkers were measured. RESULTS: HCC risk was inversely associated with intake of total fish (per 20 g/day increase, HR = 0.83, 95% CI 0.74-0.95 and HR = 0.80, 95% CI 0.69-0.97 before and after calibration, respectively). This inverse association was also suggested after adjusting for HBV/HCV status and liver function score (per 20-g/day increase, RR = 0.86, 95% CI 0.66-1.11 and RR = 0.74, 95% CI 0.50-1.09, respectively) in a nested case-control subset. Intakes of total meats or subgroups of red/processed meats, and poultry were not associated with HCC risk. CONCLUSIONS: In this large European cohort, total fish intake is associated with lower HCC 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 | 0.000 | 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 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".