Protein “requirements” beyond the RDA: implications for optimizing health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Substantial evidence supports the increased consumption of high-quality protein to achieve optimal health outcomes. A growing body of research indicates that protein intakes well above the current Recommended Dietary Allowance help to promote healthy aging, appetite regulation, weight management, and goals aligned with athletic performance. Higher protein intakes may help prevent age-related sarcopenia, the loss of muscle mass, and strength that predisposes older adults to frailty, disability, and loss of autonomy. Higher protein diets also improve satiety and lead to greater reductions in body weight and fat mass compared with standard protein diets, and may therefore serve as a successful strategy to help prevent and/or treat obesity. Athletes can also benefit from higher protein intakes to maximize athletic performance given the critical role protein plays in stimulating muscle protein remodelling after exercise. Protein quality, per meal dose, and timing of ingestion are also important considerations. Despite persistent beliefs to the contrary, we can find no evidence-based link between higher protein diets and renal disease or adverse bone health. This brief synopsis highlights recent learnings based on presentations at the 2015 Canadian Nutrition Society conference, Advances in Protein Nutrition across the Lifespan. Current evidence indicates intakes in the range of at least 1.2 to 1.6 g/(kg·day) of high-quality protein is a more ideal target for achieving optimal health outcomes in adults.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it