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Record W3009975500 · doi:10.1111/jgs.16372

Sarcopenia Definition: The Position Statements of the Sarcopenia Definition and Outcomes Consortium

2020· article· en· W3009975500 on OpenAlexaff
Shalender Bhasin, Thomas G. Travison, Todd M. Manini, Sheena Patel, Karol M. Pencina, Roger A. Fielding, Jay Magaziner, Anne B. Newman, Douglas P. Kiel, Cyrus Cooper, Jack M. Guralnik, Jane A. Cauley, Hidenori Arai, Brian C. Clark, Francesco Landi, L. Schaap, Suzette L. Pereira, Daniel Rooks, Jean Woo, Linda J. Woodhouse, Ellen F. Binder, Todd T. Brown, Michelle Shardell, Qian‐Li Xue, Ralph B. D’Agostino, Denise Orwig, Greg Gorsicki, Rosaly Correa‐de‐Araujo, Peggy M. Cawthon

Bibliographic record

VenueJournal of the American Geriatrics Society · 2020
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Alberta
FundersNational Center for Medical Rehabilitation ResearchNational Institute of Child Health and Human DevelopmentMedical Research CouncilCenters for Disease Control and PreventionNational Institute for Health and Care ResearchNational Institute on AgingCytokineticsAstellas PharmaNovartis Institutes for BioMedical ResearchNational Institute of Nursing ResearchServierNational Cancer InstitutePfizerAbbott JapanJohns Hopkins UniversityUniversity of WashingtonU.S. Department of DefenseEli Lilly and CompanyAmgenNational Institutes of HealthRegeneron PharmaceuticalsU.S. Department of AgricultureU.S. Department of Health and Human ServicesFoundation for the National Institutes of HealthMultiple Myeloma Research Foundation
KeywordsSarcopeniaMedicineGrip strengthPhysical medicine and rehabilitationPopulationLean body massPhysical therapyGaitPreferred walking speedGerontologyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To develop an evidence-based definition of sarcopenia that can facilitate identification of older adults at risk for clinically relevant outcomes (eg, self-reported mobility limitation, falls, fractures, and mortality), the Sarcopenia Definition and Outcomes Consortium (SDOC) crafted a set of position statements informed by a literature review and SDOC's analyses of eight epidemiologic studies, six randomized clinical trials, four cohort studies of special populations, and two nationally representative population-based studies. METHODS: Thirteen position statements related to the putative components of a sarcopenia definition, informed by the SDOC analyses and literature synthesis, were reviewed by an independent international expert panel (panel) iteratively and voted on by the panel during the Sarcopenia Position Statement Conference. Four position statements related to grip strength, three to lean mass derived from dual-energy x-ray absorptiometry (DXA), and four to gait speed; two were summary statements. RESULTS: The SDOC analyses identified grip strength, either absolute or scaled to measures of body size, as an important discriminator of slowness. Both low grip strength and low usual gait speed independently predicted falls, self-reported mobility limitation, hip fractures, and mortality in community-dwelling older adults. Lean mass measured by DXA was not associated with incident adverse health-related outcomes in community-dwelling older adults with or without adjustment for body size. CONCLUSION: The panel agreed that both weakness defined by low grip strength and slowness defined by low usual gait speed should be included in the definition of sarcopenia. These position statements offer a rational basis for an evidence-based definition of sarcopenia. The analyses that informed these position statements are summarized in this article and discussed in accompanying articles in this issue of the journal. J Am Geriatr Soc 68:1410-1418, 2020.

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)0.181
metaresearch head score (Gemma)0.186
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: Empirical · Consensus signal: none
Teacher disagreement score0.181
Threshold uncertainty score0.958

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1810.186
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0110.010
Science and technology studies0.0050.005
Scholarly communication0.0110.006
Open science0.0080.017
Research integrity0.0130.018
Insufficient payload (model declined to judge)0.0020.001

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 head0.060
GPT teacher head0.345
Teacher spread0.285 · 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
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

Citations677
Published2020
Admission routes1
Has abstractyes

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