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Record W2012303256 · doi:10.1080/15438627.2013.852088

Comparison of Creatine Supplementation Before Versus After Supervised Resistance Training in Healthy Older Adults

2014· article· en· W2012303256 on OpenAlexaff
Darren G. Candow, Gordon A. Zello, Binbing Ling, Jonathan P. Farthing, Philip D. Chilibeck, Katherine M. McLeod, Jonathan B. Harris, Shanthi Johnson

Bibliographic record

VenueResearch in Sports Medicine · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of SaskatchewanUniversity of Regina
Fundersnot available
KeywordsCreatineCreatine MonohydrateLean body massMedicineResistance trainingPlaceboLeg pressInternal medicineCatabolismSarcopeniaEndocrinologyMuscle massRenal functionStrength trainingPhysical therapyBody weightMetabolism

Abstract

fetched live from OpenAlex

This study was performed to compare the effects of creatine supplementation (CR) before vs. after supervised resistance training (RT) in healthy older adults. Participants were randomized to one of two groups: CR-Before (0.1g•kg(-1) creatine before + 0.1g•kg(-1) placebo [rice flour] after RT, n = 11) or CR-After (placebo before + creatine after RT, n = 11). Resistance training (RT) was performed 3 days/week, on nonconsecutive days, for 12 weeks. Prior to and following the study, measures were taken for body composition, maximum strength, muscle protein catabolism, and kidney function. Over the 12-week training period, both groups experienced a significant increase in whole-body lean tissue mass, limb muscle thickness, and upper and lower body strength and a decrease in muscle protein catabolism (p < 0.001), with no differences between groups. There was no change in kidney function over time. Changes in muscle mass or strength are similar when creatine is ingested before or after supervised resistance training in older 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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.054
GPT teacher head0.411
Teacher spread0.357 · 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 designObservational
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

Citations55
Published2014
Admission routes1
Has abstractyes

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