Dietary protein for athletes: From requirements to optimum adaptation
Bibliographic record
Abstract
Opinion on the role of protein in promoting athletic performance is divided along the lines of how much aerobic-based versus resistance-based activity the athlete undertakes. Athletes seeking to gain muscle mass and strength are likely to consume higher amounts of dietary protein than their endurance-trained counterparts. The main belief behind the large quantities of dietary protein consumption in resistance-trained athletes is that it is needed to generate more muscle protein. Athletes may require protein for more than just alleviation of the risk for deficiency, inherent in the dietary guidelines, but also to aid in an elevated level of functioning and possibly adaptation to the exercise stimulus. It does appear, however, that there is a good rationale for recommending to athletes protein intakes that are higher than the RDA. Our consensus opinion is that leucine, and possibly the other branched-chain amino acids, occupy a position of prominence in stimulating muscle protein synthesis; that protein intakes in the range of 1.3-1.8 g · kg(-1) · day(-1) consumed as 3-4 isonitrogenous meals will maximize muscle protein synthesis. These recommendations may also be dependent on training status: experienced athletes would require less, while more protein should be consumed during periods of high frequency/intensity training. Elevated protein consumption, as high as 1.8-2.0 g · kg(-1) · day(-1) depending on the caloric deficit, may be advantageous in preventing lean mass losses during periods of energy restriction to promote fat loss.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| 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".