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Record W4362474151 · doi:10.2147/cia.s404734

The Relationship Between Sarcopenia, Cognitive Impairment, and Cerebral White Matter Hyperintensity in the Elderly

2023· article· en· W4362474151 on OpenAlexaboutno aff
Kangrui Zhang, Kang-Di Zhang, Qiuwan Liu, Juncang Wu

Bibliographic record

VenueClinical Interventions in Aging · 2023
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSarcopeniaHyperintensityCognitive impairmentCognitionWhite matterPhysical medicine and rehabilitationGerontologyInternal medicineMagnetic resonance imagingPsychiatryRadiology

Abstract

fetched live from OpenAlex

Purpose: To explore the relationship between sarcopenia-related indices, cognitive impairment and cerebral white matter hyperintensities. Patients and methods: Ninety-five hospitalized older adults aged 60 years and older were used in this study. Three sarcopenia-related indicators were measured: hand grip strength (Measured with a spring-type dynamometer), gait speed (6m step speed method), and appendicular skeletal muscle mass (ASM, bioelectrical impedance). Sarcopenia was defined according to the Asian Working Group for Sarcopenia (AWGS) criteria. Cognitive function was assessed using Montreal Cognitive Assessment (MoCA). Cerebral white matter hyperintensity was assessed using 3.0T superconducting magnetic resonance imaging. Results: In both men and women, these three indices of sarcopenia were significantly and negatively correlated with WMH grades, except for appendicular skeletal muscle mass and WMH grades in women. Scores on the MoCA scale were significantly positive correlated with grip strength, and ASM, both in men and women. After adjusting for confounders and WMHs, regression analyses showed an increased incidence of cognitive impairment in patients with sarcopenia relative to those without sarcopenia. Conclusion: Lower sarcopenia-related indices were significantly associated with cognitive impairment. WMHs may be one of the factors linking sarcopenia and cognitive function.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.049
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 head0.300
GPT teacher head0.499
Teacher spread0.199 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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