Adherence to the mediterranean diet and lymphoma risk in the european prospective investigation into cancer and nutrition
Bibliographic record
Abstract
There is a growing evidence of the protective role of the Mediterranean diet (MD) on cancer. However, no prospective study has yet investigated its influence on lymphoma. We evaluated the association between adherence to the MD and risk of lymphoma and its subtypes in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. The analysis included 476,160 participants, recruited from 10 European countries between 1991 and 2001. Adherence to the MD was estimated through the adapted relative MD (arMED) score excluding alcohol. Cox proportional hazards regression models were used while adjusting for potential confounders. During an average follow-up of 13.9 years, 3,136 lymphomas (135 Hodgkin lymphoma [HL], 2,606 non-HL and 395 lymphoma not otherwise specified) were identified. Overall, a 1-unit increase in the arMED score was associated with a 2% lower risk of lymphoma (95% CI: 0.97; 1.00, p-trend = 0.03) while a statistically nonsignificant inverse association between a high versus low arMED score and risk of lymphoma was observed (hazard ratio [HR]: 0.91 [95% CI 0.80; 1.03], p-trend = 0.12). Analyses by lymphoma subtype did not reveal any statistically significant associations. Albeit with small numbers of cases (N = 135), a suggestive inverse association was found for HL (HR 1-unit increase = 0.93 [95% CI: 0.86; 1.01], p-trend = 0.07). However, the study may have lacked statistical power to detect small effect sizes for lymphoma subtype. Our findings suggest that an increasing arMED score was inversely related to the risk of overall lymphoma in EPIC but not by subtypes. Further large prospective studies are warranted to confirm these findings.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".