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Record W2170406844 · doi:10.3945/an.115.008367

Nutritional Supplements in Support of Resistance Exercise to Counter Age-Related Sarcopenia

2015· review· en· W2170406844 on OpenAlexafffund
Stuart M. Phillips

Bibliographic record

VenueAdvances in Nutrition · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsSarcopeniaMedicineMuscle massResistance trainingMuscle strengthNutritional SupplementationQuality of life (healthcare)GerontologyPhysical therapyIntensive care medicinePhysical medicine and rehabilitationInternal medicine

Abstract

fetched live from OpenAlex

Age-related sarcopenia, composed of myopenia (a decline in muscle mass) and dynapenia (a decline in muscle strength), can compromise physical function, increase risk of disability, and lower quality of life in older adults. There are no available pharmaceutical treatments for this condition, but evidence shows resistance training (RT) is a viable and relatively low-cost treatment with an exceptionally positive side effect profile. Further evidence suggests that RT-induced increases in muscle mass, strength, and function can be enhanced by certain foods, nutrients, or nutritional supplements. This brief review focuses on adjunctive nutritional strategies, which have a reasonable evidence base, to enhance RT-induced gains in outcomes relevant to sarcopenia and to reducing risk of functional declines.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
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 head0.019
GPT teacher head0.346
Teacher spread0.327 · 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

Citations142
Published2015
Admission routes2
Has abstractno

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