MétaCan
Menu
Back to cohort
Record W4281652316 · doi:10.1016/j.bone.2022.116467

Creatine supplementation for older adults: Focus on sarcopenia, osteoporosis, frailty and Cachexia

2022· review· en· W4281652316 on OpenAlexaff
Darren G. Candow, Philip D. Chilibeck, Scott C. Forbes, Ciaran M. Fairman, Bruno Gualano, Hamilton Roschel

Bibliographic record

VenueBone · 2022
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsBrandon UniversityUniversity of SaskatchewanUniversity of Regina
Fundersnot available
KeywordsSarcopeniaMedicineCreatineCachexiaOsteoporosisPhysical therapyGerontologyMuscle massInternal medicinePhysical medicine and rehabilitationCancer

Abstract

fetched live from OpenAlex

Sarcopenia refers to the age-related reduction in strength, muscle mass and functionality which increases the risk for falls, injuries and fractures. Sarcopenia is associated with other age-related conditions such as osteoporosis, frailty and cachexia. Identifying treatments to overcome sarcopenia and associated conditions is important from a global health perspective. There is evidence that creatine monohydrate supplementation, primarily when combined with resistance training, has favorable effects on indices of aging muscle and bone. These musculoskeletal benefits provide some rationale for creatine being a potential intervention for treating frailty and cachexia. The purposes of this narrative review are to update the collective body of research pertaining to the effects of creatine supplementation on indices of aging muscle and bone (including bone turnover markers) and present possible justification and rationale for its utilization in the treatment of frailty and cachexia 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 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.098
GPT teacher head0.415
Teacher spread0.317 · 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

Citations60
Published2022
Admission routes1
Has abstractno

Explore more

Same venueBoneSame topicNutrition and Health in AgingFrench-language works237,207