The Mediterranean Diet in the Prevention of Degenerative Chronic Diseases
Bibliographic record
Abstract
Degenerative chronic diseases are a problem related to the aging phenomenon of industrialized countries due to the increase of risk factors and related comorbidity such as overweight, obesity, metabolic syndrome, diabetes, hypertension and hyperlipidemia with a consequent increased risk of cardiovascular disease (CVD) and cancer. Moreover, the significant reduction of physical activity in daily life and the huge growth in food availability have considerably increased the risk of such diseases. Particular attention should be paid to primary prevention by means of health strategies based on improvement in lifestyle intervention such as implementation of Mediterranean diet and promotion of physical activity programs. In this chapter, the protective effect of Mediterranean diet and the role of certain foods and/or their constituents are analyzed; the possible mechanisms by which Mediterranean diet is effective in the prevention of cardiovascular and other chronic diseases are presented, in particular the effects exerted by antioxidants, polyphenols, fibers, unsaturated fatty acids, and alcohol. The genetic revolution in the past decades has produced new fields of study where the interaction between foods, nutrients, and our genetic makeup is investigated. The relationship between nutrigenetics and nutrigenomics and the Mediterranean diet are the future area that research should discover.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".