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Record W2129796994 · doi:10.1017/s002966511000399x

The science of muscle hypertrophy: making dietary protein count

2010· review· en· W2129796994 on OpenAlexafffund
Stuart M. Phillips

Bibliographic record

VenueProceedings of The Nutrition Society · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsMuscle hypertrophyAnabolismWhey proteinProtein biosynthesisInternal medicineEndocrinologyMuscle proteinCarbohydrateSarcopeniaHigh proteinBiologyFood scienceChemistryBiochemistryMedicineSkeletal muscle

Abstract

fetched live from OpenAlex

Growing evidence supports the conclusion that consumption of protein in close temporal proximity to the performance of resistance exercise promotes greater muscular hypertrophy. We can also state with good certainty that merely consuming energy, as carbohydrate for example, is also not sufficient to maximise muscle protein synthesis leading to anabolism and net new muscle protein accretion. Recent work also indicates that certain types of proteins, particular those that are rapidly digested and high in leucine content (i.e. whey protein), appear to be more efficient at stimulating muscle protein synthesis. Continued practice of consumption of these types or proteins after exercise should lead to greater hypertrophy. Reviews of numerous training studies indicate that studies in which milk proteins and principally whey protein show an advantage of these proteins over and above isoenergetic carbohydrate and soya protein in promoting hypertrophy. Thus, the combined evidence suggests a strategic advantage of practising early post-exercise consumption of whey protein or dairy-based protein to promote muscle protein synthesis, net muscle protein accretion and ultimately hypertrophy.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.922
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.290
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations80
Published2010
Admission routes2
Has abstractyes

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