Supplemental Protein in Support of Muscle Mass and Health: Advantage Whey
Bibliographic record
Abstract
Skeletal muscle is an integral body tissue playing key roles in strength, performance, physical function, and metabolic regulation. It is essential for athletes to ensure that they have optimal amounts of muscle mass to ensure peak performance in their given sport. However, the role of maintaining muscle mass during weight loss and as we age is an emerging concept, having implications in chronic disease prevention, functional capacity, and quality of life. Higher-protein diets have been shown to: (1) promote gains in muscle mass, especially when paired with resistance training; (2) spare muscle mass loss during caloric restriction; and (3) attenuate the natural loss of muscle mass that accompanies aging. Protein quality is important to the gain and maintenance of muscle mass. Protein quality is a function of protein digestibility, amino acid content, and the resulting amino acid availability to support metabolic function. Whey protein is one of the highest-quality proteins given its amino acid content (high essential, branched-chain, and leucine amino acid content) and rapid digestibility. Consumption of whey protein has a robust ability to stimulate muscle protein synthesis. In fact, whey protein has been found to stimulate muscle protein synthesis to a greater degree than other proteins such as casein and soy. This review examines the existing data supporting the role for protein consumption, with an emphasis on whey protein, in the regulation of muscle mass and body composition in response to resistance training, caloric restriction, and aging.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".