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Record W3091967282 · doi:10.1017/s0029665120007892

Nutrient-dense protein as a primary dietary strategy in healthy ageing: please sir, may we have more?

2020· review· en· W3091967282 on OpenAlexaff
Everson Araújo Nunes, Brad S. Currier, Changhyun Lim, Stuart M. Phillips

Bibliographic record

VenueProceedings of The Nutrition Society · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMcMaster University
FundersNational Research Foundation of KoreaConselho Nacional de Desenvolvimento Científico e TecnológicoNational Research Foundation
KeywordsIngestionAgeingSkeletal muscleMuscle massSarcopeniaMuscle proteinMedicineDietary proteinGerontologyNutrientPhysiologyBiologyEndocrinologyInternal medicine

Abstract

fetched live from OpenAlex

A progressive decrement in muscle mass and muscle function, sarcopoenia, accompanies ageing. The loss of skeletal muscle mass and function is the main feature of sarcopoenia. Preventing the loss of muscle mass is relevant since sarcopoenia can have a significant impact on mobility and the quality of life of older people. Dietary protein and physical activity have an essential role in slowing muscle mass loss and helping to maintain muscle function. However, the current recommendations for daily protein ingestion for older persons appear to be too low and are in need of adjustment. In this review, we discuss the skeletal muscle response to protein ingestion, and review the data examining current dietary protein recommendations in the older subjects. Furthermore, we review the concept of protein quality and the important role that nutrient-dense protein (NDP) sources play in meeting overall nutrient requirements and improving dietary quality. Overall, the current evidence endorses an increase in the daily ingestion of protein with emphasis on the ingestion of NDP choices by 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.833
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.035
GPT teacher head0.306
Teacher spread0.272 · 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 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

Citations19
Published2020
Admission routes1
Has abstractyes

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