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Record W4280574255 · doi:10.3389/fspor.2022.893714

Creatine O'Clock: Does Timing of Ingestion Really Influence Muscle Mass and Performance?

2022· review· en· W4280574255 on OpenAlexafffund
Darren G. Candow, Scott C. Forbes, Michael D. Roberts, Brian D. Roy, José António, Abbie E. Smith‐Ryan, Eric S. Rawson, Bruno Gualano, Hamilton Roschel

Bibliographic record

VenueFrontiers in Sports and Active Living · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsBrock UniversityBrandon UniversityUniversity of Regina
FundersBrandon University
KeywordsCreatineIngestionCreatine MonohydrateInternal medicineResistance trainingEndocrinologyMedicinePlaceboPathology

Abstract

fetched live from OpenAlex

It is well-established that creatine supplementation augments the gains in muscle mass and performance during periods of resistance training. However, whether the timing of creatine ingestion influences these physical and physiological adaptations is unclear. Muscle contractions increase blood flow and possibly creatine transport kinetics which has led some to speculate that creatine in close proximity to resistance training sessions may lead to superior improvements in muscle mass and performance. Furthermore, creatine co-ingested with carbohydrates or a mixture of carbohydrates and protein that alter insulin enhance creatine uptake. The purpose of this narrative review is to (i) discuss the purported mechanisms and variables that possibly justify creatine timing strategies, (ii) to critically evaluate research examining the strategic ingestion of creatine during a resistance training program, and (iii) provide future research directions pertaining to creatine timing.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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 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

Citations22
Published2022
Admission routes2
Has abstractyes

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