MétaCan
Menu
Back to cohort
Record W4415461255 · doi:10.1002/jcsm.70108

Comment on ‘Systematic Review and Meta‐Analysis of Protein Intake to Support Muscle Mass and Function in Healthy Adults’ by Nunes et al.—The Authors' Reply

2025· article· en· W4415461255 on OpenAlexaffabout
Everson Araújo Nunes, Daniel Tomé, Sandra Naranjo‐Modad, Diana Sherifali, Claire Gaudichon, Stuart M. Phillips

Bibliographic record

VenueJournal of Cachexia Sarcopenia and Muscle · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMcMaster UniversityUniversity of Guelph
Fundersnot available
KeywordsAssertionIntervention (counseling)Causal inferenceInferenceRobustness (evolution)Systematic errorRandomized controlled trialMeta-analysis

Abstract

fetched live from OpenAlex

regarding our systematic review and meta-analysis published in 2022 [2].The concerns these authors raise essentially misinterpret our methodology, miss key details clearly described in our paper, and fail to recognize the robustness of our analytical approach.

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)0.038
metaresearch head score (Gemma)0.229
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.052
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.229
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0040.003
Science and technology studies0.0040.006
Scholarly communication0.0060.008
Open science0.0070.005
Research integrity0.0520.049
Insufficient payload (model declined to judge)0.0090.009

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 head0.015
GPT teacher head0.292
Teacher spread0.277 · 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
GenreCommentary

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
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Cachexia Sarcopenia and MuscleSame topicMuscle metabolism and nutritionFrench-language works237,207