Adherence to the Mediterranean Diet and the Risk of Head and Neck Cancer: A Systematic Review and Meta-Analysis of Case–Control Studies
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Head and neck cancer (HNC) is the seventh most common cancer worldwide, with rising incidence rates and significant mortality. While tobacco use, alcohol consumption, and viral infections are established risk factors, the role of dietary patterns, particularly adherence to the Mediterranean diet (MD), in HNC prevention has gained increasing attention. The aim of the current systematic review and meta-analysis is to investigate the association between adherence to the MD and the risk of HNC. METHODS: A comprehensive search was conducted, following PRISMA guidelines, to identify relevant studies published up to January 2024 that assessed the association between MD adherence and HNC risk in adults. Pooled odds ratios (OR) for a three-unit increase in MD adherence scores and corresponding 95% confidence intervals (CI) were calculated using a random-effects model. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). RESULTS: = 92%). Individual component analyses from three studies revealed that higher fruit and vegetable consumption significantly decreased HNC risk, whereas legumes, fish, and low meat intake showed no statistically significant associations. CONCLUSIONS: Adherence to the Mediterranean diet is associated with a significantly reduced risk of head and neck cancer. These findings support the role of the MD in cancer prevention and highlight the potential benefits of MD adherence in reducing HNC risk. Further prospective studies and randomized controlled trials are needed to confirm these findings and explore the underlying mechanisms.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".