Common questions and misconceptions about protein supplementation: what does the scientific evidence really show?
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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