Common questions and misconceptions about protein supplementation: what does the scientific evidence really show?
Bibliographic record
Abstract
Protein supplementation often refers to increasing the intake of this particular macronutrient through dietary supplements in the form of powders, ready-to-drink shakes, and bars. The primary purpose of protein supplementation is to augment dietary protein intake, aiding individuals in meeting their protein requirements, especially when it may be challenging to do so through regular food (i.e. chicken, beef, fish, pork, etc.) sources alone. A large body of evidence shows that protein has an important role in exercising and sedentary individuals. A PubMed search of “protein and exercise performance” reveals thousands of publications. Despite the considerable volume of evidence, it is somewhat surprising that several persistent questions and misconceptions about protein exist. The following are addressed: 1) Is protein harmful to your kidneys? 2) Does consuming “excess” protein increase fat mass? 3) Can dietary protein have a harmful effect on bone health? 4) Can vegans and vegetarians consume enough protein to support training adaptations? 5) Is cheese or peanut butter a good protein source? 6) Does consuming meat (i.e., animal protein) cause unfavorable health outcomes? 7) Do you need protein if you are not physically active? 8) Do you need to consume protein ≤ 1 hour following resistance training sessions to create an anabolic environment in skeletal muscle? 9) Do endurance athletes need additional protein? 10) Does one need protein supplements to meet the daily requirements of exercise-trained individuals? 11) Is there a limit to how much protein one can consume in a single meal? To address these questions, we have conducted a thorough scientific assessment of the literature concerning protein supplementation.
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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 | 0.159 | 0.503 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".