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Record W3037537779 · doi:10.3390/nu12061880

Effects of Creatine Supplementation during Resistance Training Sessions in Physically Active Young Adults

2020· article· en· W3037537779 on OpenAlexaff
Scotty Mills, Darren G. Candow, Scott C. Forbes, J. Patrick Neary, Michael J. Ormsbee, José António

Bibliographic record

VenueNutrients · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsBrandon UniversityUniversity of Regina
Fundersnot available
KeywordsResistance trainingCreatineMedicinePhysical therapyTraining (meteorology)Physical medicine and rehabilitationInternal medicinePhysics

Abstract

fetched live from OpenAlex

The purpose was to examine the effects of creatine supplementation during resistance training sessions on skeletal muscle mass and exercise performance in physically active young adults. Twenty-two participants were randomized to supplement with creatine (CR: n = 13, 26 ± 4 yrs; 0.0055 g·kg−1 post training set) or placebo (PLA: n = 9, 26 ± 5 yrs; 0.0055 g·kg−1 post training set) during six weeks of resistance training (18 sets per training session; five days per week). Prior to and following training and supplementation, measurements were made for muscle thickness (elbow and knee flexors/extensors, ankle plantarflexors), power (vertical jump and medicine ball throw), strength (leg press and chest press one-repetition maximum (1-RM)) and muscular endurance (one set of repetitions to volitional fatigue using 50% baseline 1-RM for leg press and chest press). The creatine group experienced a significant increase (p < 0.05) in leg press, chest press and total body strength and leg press endurance with no significant changes in the PLA group. Both groups improved total body endurance over time (p < 0.05), with greater gains observed in the creatine group. In conclusion, creatine ingestion during resistance training sessions is a viable strategy for improving muscle strength and some indices of muscle endurance in physically active young adults.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.007
GPT teacher head0.238
Teacher spread0.232 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations59
Published2020
Admission routes1
Has abstractyes

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