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Record W3037372096 · doi:10.3390/nu12061890

Effects of Dietary Protein on Body Composition in Exercising Individuals

2020· review· en· W3037372096 on OpenAlexaff
José António, Darren G. Candow, Scott C. Forbes, Michael J. Ormsbee, Patrick G. Saracino, Justin Roberts

Bibliographic record

VenueNutrients · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsBrandon UniversityUniversity of Regina
Fundersnot available
KeywordsComposition (language)Dietary proteinEndocrinologyInternal medicineMedicineNarrative reviewMuscle proteinDietary Reference IntakeFat massBiologyPhysiologySkeletal muscleBody weightNutrient

Abstract

fetched live from OpenAlex

Protein is an important component of a healthy diet and appears to be integral to enhancing training adaptations in exercising individuals. The purpose of this narrative review is to provide an evidence-based assessment of the current literature examining increases in dietary protein intake above the recommended dietary allowance (RDA: 0.8 g/kg/d) in conjunction with chronic exercise on body composition (i.e., muscle, fat and bone). We also highlight acute and chronic pre-sleep protein studies as well as the influence of exercise timing on body composition. Overall, a high-protein diet appears to increase muscle accretion and fat loss and may have beneficial effects on bone when combined with exercise. Pre-sleep protein is a viable strategy to help achieve total daily protein goals. Importantly, there appears to be no deleterious effects from a high-protein diet on muscle, fat or bone in exercising individuals.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.017
GPT teacher head0.291
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2020
Admission routes1
Has abstractyes

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