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Record W4400149458 · doi:10.1016/j.cdnut.2024.102781

Healthy Older Adults Consuming the Protein RDA Show Similar Whole-Body Protein Kinetics While Consuming Vegan and Lacto-Vegetarian Diets

2024· article· en· W4400149458 on OpenAlexaff
Everson Araújo Nunes, Changhyun Lim, James McKendry, F. P. Costa, Linda May, Delaney Bourget, Maya Tsipris, David D. Church, Arny A. Ferrando, Stuart M. Phillips

Bibliographic record

VenueCurrent Developments in Nutrition · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMcMaster University
FundersNational Institutes of Health
KeywordsFood scienceVegan DietChemistryMedicineInternal medicine

Abstract

fetched live from OpenAlex

Objectives: Dairy products are sources of high-quality proteins (i.e., rich in essential amino acids – EAA) and are commonly incorporated in flexible vegetarian diets to improve overall protein quality. However, no previous studies have measured the impact of replacing part of a 100% plant-based diet (i.e., vegan) with dairy on whole-body protein kinetics in older persons. We hypothesized that replacing plant-based protein sources with dairy-source proteins would enhance the whole-body anabolic response to meals.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.014
GPT teacher head0.266
Teacher spread0.251 · 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 designObservational
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
Published2024
Admission routes1
Has abstractno

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