754 The role of milk fat in modern human nutrition: What is the current state of knowledge?
Bibliographic record
Abstract
Milk is a good source of fats and proteins as well as essential nutrients such as calcium and phosphorus. Milk has been an important part of the human diet since the domestication of animals. Milk consumption, particularly cow's milk, is at an all-time high in western societies, and steadily increasing in societies undergoing nutrition transition. Milk is regarded as an important component of a healthy diet especially during childhood and adolescence. In recent times, controversy abounds regarding the role of dairy products, especially dairy fat, in cardiovascular health as many recent studies have reported inconsistent findings. Here, we present a review of the current state of knowledge focusing on effects of milk fat and fatty acid compositions, biomarkers for fatty acid intake and microRNA in milk exosomes, on human health. We suggest that interdisciplinary approaches and trans-sectorial collaboration are needed to clarify the role of milk fat in human health. The quality and composition of milk fat can vary widely, even between individual animals in the same breed. Constraints and potential for modulation of milk fatty acid composition by nutritional and genetic strategies will be discussed. Finally, we propose that dairy foods, including regular-fat milk and other dairy products, can remain as an important component of an overall healthy dietary pattern for a majority of individuals across the lifespan.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".