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Record W4401053129 · doi:10.1007/s40520-024-02798-4

An executive summary on the Global conceptual definition of Sarcopenia

2024· article· en· W4401053129 on OpenAlexaff
Ben Kirk, Peggy M. Cawthon, Hidenori Arai, José Alberto Ávila‐Funes, Rocco Barazzoni, Shalender Bhasin, Ellen F. Binder, Olivier Bruyère, Tommy Cederholm, Liang‐Kung Chen, Cyrus Cooper, Gustavo Duque, Roger A. Fielding, Jack M. Guralnik, Douglas P. Kiel, Francesco Landi, Jean‐Yves Reginster, Avan Aihie Sayer, Marjolein Visser, Stephan von Haehling, Jean Woo, Alfonso J. Cruz‐Jentoft

Bibliographic record

VenueAging Clinical and Experimental Research · 2024
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesBiotechnology and Biological Sciences Research CouncilEuropean Geriatric Medicine SocietyEuropean Society for Clinical Nutrition and MetabolismInternational Osteoporosis FoundationNational Institute for Health and Care ResearchAmerican Society for Bone and Mineral Research
KeywordsSarcopeniaExecutive summaryPsychologyMedicineBusinessInternal medicine

Abstract

fetched live from OpenAlex

International audience

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)0.018
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.030
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.006
Science and technology studies0.0020.003
Scholarly communication0.0070.007
Open science0.0030.005
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0320.015

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 head0.387
GPT teacher head0.577
Teacher spread0.190 · 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 designTheoretical or conceptual
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

Citations11
Published2024
Admission routes1
Has abstractyes

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