Consumption of fruits, vegetables and fruit juices and differentiated thyroid carcinoma risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) study
Bibliographic record
Abstract
Fruit and vegetable (F&V) intake is considered as probably protective against overall cancer risk, but results in previous studies are not consistent for thyroid cancer (TC). The purpose of this study is to examine the association between the consumption of fruits, vegetables, fruit juices and differentiated thyroid cancer risk within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. The EPIC study is a cohort including over half a million participants, recruited between 1991 and 2000. During a mean follow-up of 14 years, 748 incident first primary differentiated TC cases were identified. F&V and fruit juice intakes were assessed through validated country-specific dietary questionnaires. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox regression models adjusted for potential confounding factors. Comparing the highest versus lowest quartile of intake, differentiated TC risk was not associated with intakes of total F&V (HR: 0.89; 95% CI: 0.68-1.15; p-trend = 0.44), vegetables (HR: 0.89; 95% CI: 0.69-1.14; p-trend = 0.56), or fruit (HR: 1.00; 95% CI: 0.79-1.26; p-trend = 0.64). No significant association was observed with any individual type of vegetable or fruit. However, there was a positive borderline trend with fruit juice intake (HR: 1.23; 95% CI: 0.98-1.53; p-trend = 0.06). This study did not find any significant association between F&V intakes and differentiated TC risk; however a positive trend with fruit juice intake was observed, possibly related to its high sugar content.
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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".