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Record W4310612944 · doi:10.5770/cgj.25.608

Assessing the Effects of Dietary Protein Supplementation on Sarcopenia in Community-Dwelling Older Adults

2022· review· en· W4310612944 on OpenAlexaffvenue
Vincent Hou, Kenneth Madden

Bibliographic record

VenueCanadian Geriatrics Journal · 2022
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsVancouver Coastal HealthVancouver Coastal Health Research InstituteUniversity of British Columbia
Fundersnot available
KeywordsSarcopeniaMedicineMuscle massGerontologyNutritional SupplementationMuscle strengthIntervention (counseling)Physical therapyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Sarcopenia, an age-associated skeletal muscle disorder characterized by muscle loss, is associated with disability in elderly populations. This literature review summarizes the impact of protein intake and supplementation on the indicators of severe sarcopenia-muscle mass, muscle strength, and physical function in community-dwelling older adults. We performed a literature search on PubMed, EMBASE, and MedLine, and included studies that evaluated the effects of protein intake with or without exercise intervention and on sarcopenia in community-dwelling older adults. Information regarding study participants, protein intervention, and sarcopenia-related outcomes were collected. Protein supplementation with or without exercise positively improves muscle mass, and aspects of muscle strength and physical function in sarcopenic and pre-frail older adults, while it elicited inconclusive effects in healthy populations. Greater dietary animal-based and soy-based protein diets can improve muscle mass in older adults. In conclusion, protein supplementation can improve muscle mass and reduce the risk of sarcopenia in sarcopenia and pre-frail older adults, while future studies should continue to investigate the effects of protein supplementation on indicators of sarcopenia in healthy 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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
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 head0.078
GPT teacher head0.396
Teacher spread0.318 · 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.

Study designOther design
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

Citations15
Published2022
Admission routes2
Has abstractyes

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