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Record W4412489008 · doi:10.1080/15502783.2025.2534130

Creatine monohydrate supplementation for older adults and clinical populations

2025· review· en· W4412489008 on OpenAlexaff
Darren G. Candow, Sergej M. Ostojić, Philip D. Chilibeck, Igor Longobardi, Bruno Gualano, Mark A. Tarnopolsky, Theo Wallimann, Terence Moriarty, Richard B. Kreider, Scott C. Forbes, Uwe Schlattner, José António

Bibliographic record

VenueJournal of the International Society of Sports Nutrition · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMcMaster UniversityBrandon UniversityMcMaster Children's HospitalUniversity of SaskatchewanUniversity of Regina
Fundersnot available
KeywordsSarcopeniaMedicineCreatineLean body massClinical nutritionMuscle massPhysical therapyOsteoporosisCreatine MonohydratePhysical medicine and rehabilitationGerontologyPsychological interventionCognitionPhysiologyInternal medicinePathologyAlternative medicineBody weightNursing

Abstract

fetched live from OpenAlex

BACKGROUND: The biological process of aging is typically associated with a decrease in muscle quantity, muscle performance (primarily strength), bone mass and architecture, functionality and neurological/cognitive function. From a healthy aging perspective, interventions that have the potential to overcome or attenuate these decrements are clinically relevant. METHODS: We conducted a narrative review on the efficacy of creatine monohydrate supplementation (CrM) in older adults. RESULTS: Accumulating research shows that CrM, primarily when combined with exercise training, is safe and has beneficial effects on measures of whole-body lean body mass, regional muscle size, muscle strength, bone area and thickness, functional ability, glucose kinetics, cognition and memory. CONCLUSION: CrM has multiple benefits in older adults and may have application for treating age-related sarcopenia, osteoporosis, frailty, and those with metabolic and neuromuscular disorders.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.371
Teacher spread0.344 · 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 designNot applicable
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

Citations14
Published2025
Admission routes1
Has abstractyes

Explore more

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