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Record W4387576638 · doi:10.3390/nu15204343

Resistance Exercise and Creatine Supplementation on Fat Mass in Adults < 50 Years of Age: A Systematic Review and Meta-Analysis

2023· review· en· W4387576638 on OpenAlexaff
Darren G. Candow, Konstantinos Prokopidis, Scott C. Forbes, Flavia Rusterholz, Bill Campbell, Sergej M. Ostojić

Bibliographic record

VenueNutrients · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsBrandon UniversityUniversity of Regina
Fundersnot available
KeywordsMeta-analysisResistance trainingCreatinePhysical activityMedicineFat massGerontologyInternal medicinePhysical therapyPhysiologyEndocrinologyBody mass index

Abstract

fetched live from OpenAlex

The combination of resistance exercise and creatine supplementation has been shown to decrease body fat percentage in adults ≥ 50 years of age. However, the effect on adults < 50 years of age is currently unknown. To address this limitation, we systematically reviewed the literature and performed several meta-analyses comparing studies that included resistance exercise and creatine supplementation to resistance exercise and placebo on fat mass and body fat percentage Twelve studies were included, involving 266 participants. Adults (<50 years of age) who supplemented with creatine and performed resistance exercise experienced a very small, yet significant reduction in body fat percentage (−1.19%, p = 0.006); however, no difference was found in absolute fat mass (−0.18 kg, p = 0.76). Collectively, in adults < 50 years of age, the combination of resistance exercise and creatine supplementation produces a very small reduction in body fat percentage without a corresponding decrease in absolute fat mass.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.016
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.039
GPT teacher head0.327
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations10
Published2023
Admission routes1
Has abstractyes

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