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Record W4304811938 · doi:10.21926/rpn.2204021

The Effects of Creatine Supplementation on Markers of Muscle Damage and Inflammation Following Exercise in Older Adults: A Brief Narrative Review

2022· article· en· W4304811938 on OpenAlexaff
Dean M. Cordingley, Stephen M. Cornish

Bibliographic record

VenueRecent Progress in Nutrition · 2022
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsUniversity of ManitobaPan Am Clinic
Fundersnot available
KeywordsSarcopeniaInflammationMedicineSkeletal muscleMuscle damageCreatineNarrative reviewPopulationMuscle massInternal medicinePhysical therapyBioinformaticsPhysical medicine and rehabilitationPhysiologyIntensive care medicineBiology

Abstract

fetched live from OpenAlex

Exercise induced muscle damage occurs following strenuous and unfamiliar exercise and results in biomarkers of muscle damage and inflammation in the circulation. Creatine (Cr) is a commonly utilized nutritional supplement which has been proposed to enhance post-exercise recovery and has been suggested to decrease exercise induced inflammation. Exercise is well recognized to be beneficial for older adults to maintain skeletal muscle mass and strength as well as promote health for other biological systems. However, older adults can experience chronic low-grade inflammation, sometimes referred to as ‘inflammaging’. Therefore, it may be prudent to limit post-exercise induced skeletal muscle damage and inflammation for the older adult population who may already be in a pro-inflammatory state and at risk of age-related muscle loss (sarcopenia). The purpose of this brief narrative review is to outline the current research on Cr and its effects on biomarkers of muscle damage and inflammation in older adults. Further, the review will suggest areas of research that are required to fully understand how Cr supplementation may affect muscle damage and inflammatory biomarkers in older adults who exercise.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.239

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 head0.008
GPT teacher head0.306
Teacher spread0.297 · 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 designOther design
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

Citations0
Published2022
Admission routes1
Has abstractyes

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