MétaCan
Menu
Back to cohort
Record W2893015560 · doi:10.1080/15438627.2018.1523167

High-protein diets in trained individuals

2018· review· en· W2893015560 on OpenAlexaboutno aff
José António

Bibliographic record

VenueResearch in Sports Medicine · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsAthletesAllowance (engineering)Reference Daily IntakeDietary Reference IntakeBody weightMedicineKilogramAnimal scienceGerontologyEnvironmental healthPhysical therapyBiologyEndocrinologyNutrientEngineeringOperations management

Abstract

fetched live from OpenAlex

The United States (US) recommended dietary allowance (RDA) for protein is 0.8 grams per kilogram body weight per day (g/kg/d). The International Society of Sports Nutrition (ISSN) recently recommended an intake of 1.4-2.0 g/kg/d whereas the United States and Canadian Dietetic Association typically recommend a lower range of 1.2 to 1.7 g/kg/d. It is clear that the US RDA for protein is grossly inadequate for exercising individuals; thus, athletes are typically advised to consume twice the RDA. This falls within the range commonly recommended by academic societies. The effect of protein consumption that exceeds these aforementioned guidelines is not entirely known. This review examines the current literature as it pertains to the influence of very high protein intakes in trained individuals (i.e., humans). It is the scientific opinion of the author that athletes should consume at least 2.2 g/kg/d of protein.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.118
GPT teacher head0.434
Teacher spread0.316 · 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.

Study designOther design
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

Citations15
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueResearch in Sports MedicineSame topicMuscle metabolism and nutritionFrench-language works237,207