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Record W4413966477 · doi:10.5770/cgj.28.827

Usefulness of Muscle Stiffness Evaluated by Ultrasonography for Detecting Muscle Change Related to Contracture in Frail Older Adults

2025· article· en· W4413966477 on OpenAlexvenueno aff
Koichi Nakagawa, Hideki Kataoka, Kyo Goto, Junichiro Yamashita, Yuki Nishi, Y. Honda, Junya Sakamoto, Tomoki Origuchi, Minoru Okita

Bibliographic record

VenueCanadian Geriatrics Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsMedicineMuscle contractureContractureUltrasonographyJoint ContractureRange of motionPhysical therapyMuscle stiffnessPhysical medicine and rehabilitationStiffnessSurgery

Abstract

fetched live from OpenAlex

Background: Ultrasonography can be used to evaluate the echo intensity (EI) and strain ratio (SR) to identify muscle quality and stiffness, respectively. EI and SR are affected by aging, frailty, and joint angle positions. We investigated the effects of aging and frailty on muscle EI and SR at different joint angle positions, and identified a useful measurement method to reflect muscle changes related to contractures in frail older adults. Methods: This study had a cross-sectional design. Twenty-two healthy young adults (young group), 22 non-frail older adults (non-frail group), and 22 frail older adults (frail group) participated in this study. The range of motion (ROM) of hip abduction, EI, and SR of the adductor longus in the zero- and full-abduction positions were measured. To investigate the effects of aging and frailty, the Kruskal-Wallis test, followed by the post-hoc Steel-Dwass test, was used. In addition, to confirm whether EI and SR in each position were useful for assessing contracture, Spearman's correlation test was used. Results: < .001) showed strong and moderate correlation with the ROM, respectively. Conclusions: SR in full-abduction is affected by both frailty and aging and is useful for evaluating muscle stiffness related to contracture in frail older adults.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
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 head0.028
GPT teacher head0.306
Teacher spread0.278 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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