Yogurt and health: a focus on its matrix
Bibliographic record
Abstract
Research and clinical experience in nutrition have emphasized the merits of some dietary patterns, e.g., the Mediterranean diet, to obtain health-related benefits. This has also been the case of specific foods such as yogurt which has been tested using different approaches and under various conditions. As described in this paper, there is a quasi-consensus among population studies about the protective relationship of regular yogurt consumption with incidence of overweight and type 2 diabetes. This is concordant with laboratory-based experimentation and clinical trials showing that yogurt consumption induces favorable effects on many key components of metabolic homeostasis and energy balance. The benefits of yogurt consumption also seem to be partly explained by its status of fermented food which involves the influence of yogurt on the gut microbiota and the reciprocal role of some bacterial-derived molecules on metabolic regulation. In clinical nutrition, novel education approaches benefit from the versatility of yogurt and its high nutrient density to promote healthy eating behaviors and habits as well as cardiometabolic benefits. In summary, yogurt, especially plain yogurt, should be part of healthy eating habits because of its high nutritional value, its flexible food matrix, its beneficial bacterial components, and its versatility under usual feeding conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".