Optimizing Adult Protein Intake During Catabolic Health Conditions
Bibliographic record
Abstract
The DRIs define a range of acceptable dietary intakes for each nutrient. The range is defined from the minimum intake to avoid risk of inadequacy (i.e., the RDA) up to an upper limit (UL) based on a detectable risk of adverse effects. For most nutrients, the minimum RDA is based on alleviating a clear deficiency condition, whereas higher intakes are often recommended to optimize specific health outcomes. Evidence is accumulating that similar logic should be applied to dietary recommendations for protein. Although the RDA for protein of 0.8 g/kg body weight is adequate to avoid obvious inadequacies, multiple studies provide evidence that many adults may benefit from protein quantity, quality, and distribution beyond guidelines currently defined by the RDA. Further, the dietary requirement for protein is a surrogate for the constituent amino acids and, in particular, the 9 considered to be indispensable. Leucine provides an important example of an essential amino acid where the RDA of 42 mg/kg body weight is significantly less than the 100-110 mg/kg required to optimize metabolic regulation and skeletal muscle protein synthesis. This review will highlight the benefits of higher protein diets to optimize health during aging, inactivity, bed rest, or metabolic dysfunction such as type 2 diabetes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".