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Record W2979278189 · doi:10.14283/jfa.2019.33

Clinically Meaningful Change for Physical Performance: Perspectives of the ICFSR Task Force

2020· article· en· W2979278189 on OpenAlexaff
J. Guralnik, Karen Bandeen‐Roche, Shalendar Bhasin, Sonya Eremenco, Francesco Landi, John Muscedere, Subashan Perera, Jean‐Yves Reginster, Linda J. Woodhouse, Bruno Vellas

Bibliographic record

VenueThe Journal of Frailty & Aging · 2020
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of AlbertaQueen's University
FundersNational Institute on Aging
KeywordsSarcopeniaMedicineClinical trialPsychological interventionTask forceTask (project management)Physical medicine and rehabilitationMiamiPhysical therapyGerontologyPathologyNursingInternal medicine

Abstract

fetched live from OpenAlex

For clinical studies of sarcopenia and frailty, clinically meaningful outcome measures are needed to monitor disease progression, evaluate efficacy of interventions, and plan clinical trials. Physical performance measures including measures of gait speed and other aspects of mobility and strength have been used in many studies, although a definition of clinically meaningful change in performance has remained unclear. The International Conference on Frailty and Sarcopenia Research Task Force (ICFSR-TF), a group of academic and industry scientists investigating frailty and sarcopenia, met in Miami Beach, Florida, USA in February 2019 to explore approaches for establishing clinical meaningfulness in a manner aligned with regulatory authorities. They concluded that clinical meaningful change is contextually dependent, and that both anchor- based and distribution-based methods of quantifying physical function are informative and should be evaluated relative to patient-reported outcomes. In addition, they identified additional research needed to enable setting criteria for clinical meaningful change in trials.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.134
GPT teacher head0.390
Teacher spread0.256 · 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 designQualitative
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

Citations80
Published2020
Admission routes1
Has abstractyes

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