MétaCan
Menu
Back to cohort
Record W2030380421 · doi:10.1007/s40279-014-0254-y

A Brief Review of Higher Dietary Protein Diets in Weight Loss: A Focus on Athletes

2014· review· en· W2030380421 on OpenAlexafffund
Stuart M. Phillips

Bibliographic record

VenueSports Medicine · 2014
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 ResearchGran Sasso Science InstituteDairy Farmers of CanadaPepsiCo
KeywordsAthletesEnergy balanceWeight lossLean body massFlexibility (engineering)Balance (ability)NutrientFood scienceEnergy expenditureChemistryMedicineObesityBody weightPhysical therapyEndocrinologyBiologyMathematicsEcology

Abstract

fetched live from OpenAlex

Thermodynamics dictates that for body weight (i.e. stored substrate) loss to occur a person must ingest less energy than they expend. Athletes, who owing to their oftentimes large daily energy expenditures, may have greater flexibility than non-athletes in this regard; however, they may also have different goals for weight loss. In particular, weight lost may be less important to an athlete than from which compartment the weight is lost: fat or lean. A critical question is thus, what balance of macronutrients might promote a greater fat loss, a relative retention of lean mass, and still allow athletic performance to remain uncompromised? It is the central thesis of this review that dietary protein should be a nutrient around which changes in macronutrient composition should be framed. The requirement for protein to sustain lean mass increases while in negative energy balance and protein, as macronutrient, may have advantages with respect to satiety during energy balance, and it may allow greater fat loss during a negative energy balance. However, athletes should be mindful of the fact that increasing dietary protein intake while in negative energy balance would come at the 'expense' of another macronutrient. Most recently there has been interest in lower carbohydrate diets, which may not allow performance to be sustained given the importance of dietary carbohydrate in high-intensity exercise. The relative merits of higher protein diets for athletes are discussed.

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 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 score0.886
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.301
Teacher spread0.283 · 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

Citations76
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueSports MedicineSame topicMuscle metabolism and nutritionFrench-language works237,207