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Record W2920920502 · doi:10.1093/gerona/glz081

Establishing the Link Between Lean Mass and Grip Strength Cut Points With Mobility Disability and Other Health Outcomes: Proceedings of the Sarcopenia Definition and Outcomes Consortium Conference

2019· article· en· W2920920502 on OpenAlexaff
Peggy M. Cawthon, Thomas G. Travison, Todd M. Manini, Sheena Patel, Karol M. Pencina, Roger A. Fielding, Jay Magaziner, Anne B. Newman, Todd T. Brown, Douglas P. Kiel, Steve Cummings, Michelle Shardell, Jack M. Guralnik, Linda J. Woodhouse, Marco Pahor, Ellen F. Binder, Ralph B. D’Agostino, Xue Quian-Li, Eric Orwoll, Francesco Landi, Denise Orwig, L. Schaap, Nancy K. Latham, Vasant Hirani, Timothy Kwok, Suzette L. Pereira, Daniel Rooks, Makoto Kashiwa, Moises Torres‐Gonzalez, Joseph P. Menetski, Rosaly Correa‐de‐Araujo, Shalender Bhasin

Bibliographic record

VenueThe Journals of Gerontology Series A · 2019
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Alberta
FundersNational Institute on AgingNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Allergy and Infectious DiseasesMedical Research CouncilCenters for Disease Control and PreventionGöteborgs LäkaresällskapU.S. Department of Health and Human ServicesNational Institutes of HealthChinese University of Hong KongNational Institute of Neurological Disorders and StrokeNovo NordiskNational Center for Advancing Translational SciencesNederlandse Organisatie voor Wetenschappelijk OnderzoekU.S. Department of Veterans AffairsSchool of Medicine, Boston UniversityStiftelsen för Strategisk ForskningNational Health and Medical Research CouncilNovo Nordisk FondenNational Heart, Lung, and Blood InstituteVetenskapsrådet
KeywordsSarcopeniaGrip strengthLean body massGerontologyLink (geometry)Physical medicine and rehabilitationMedicinePsychologyPhysical therapyComputer scienceBody weightInternal medicineComputer network

Abstract

fetched live from OpenAlex

BACKGROUND: Lack of consensus on how to diagnose sarcopenia has limited the ability to diagnose this condition and hindered drug development. The Sarcopenia Definitions and Outcomes Consortium (SDOC) was formed to develop evidence-based diagnostic cut points for lean mass and/or muscle strength that identify people at increased risk of mobility disability. We describe here the proceedings of a meeting of SDOC and other experts to discuss strategic considerations in the development of evidence-based sarcopenia definition. METHODS: Presentations and panel discussions reviewed the usefulness of sarcopenia as a biomarker, the analytical approach used by SDOC to establish cut points, and preliminary findings, and provided strategic direction to develop an evidence-based definition of sarcopenia. RESULTS: The SDOC assembled data from eight epidemiological cohorts consisting of 18,831 participants, clinical populations from 10 randomized trials and observational studies, and 2 nationally representative cohorts. In preliminary assessments, grip strength or grip strength divided by body mass index was identified as discriminators of risk for mobility disability (walking speed <0.8 m/s), whereas dual-energy X-ray absorptiometry-derived lean mass measures were not good discriminators of mobility disability. Candidate definitions based on grip strength variables were associated with increased risk of mortality, falls, mobility disability, and instrumental activities of daily living disability. The prevalence of low grip strength increased with age. The attendees recommended the establishment of an International Expert Panel to review a series of position statements on sarcopenia definition that are informed by the findings of the SDOC analyses and synthesis of literature. CONCLUSIONS: International consensus on an evidence-based definition of sarcopenia is needed. Grip strength-absolute or adjusted for body mass index-is an important discriminator of mobility disability and other endpoints. Additional research is needed to develop a predictive risk model that takes into account sarcopenia components as well as age, sex, race, and comorbidities.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.086
GPT teacher head0.349
Teacher spread0.262 · 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.

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

Citations128
Published2019
Admission routes1
Has abstractyes

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