MétaCan
Menu
Back to cohort
Record W4417494176 · doi:10.3390/life16010004

Higher Prevalence of Sarcopenia in Knee Osteoarthritis and Its Association with Femoral Intercondylar Cartilage Thickness and Functional Outcomes

2025· article· en· W4417494176 on OpenAlexaboutno aff
Guan‐Bo Chen, Chien-Hui Li, Ya-Chun Hu, Yi-Ju Tsai, Yahui Chen, Sheng‐Hui Tuan

Bibliographic record

VenueLife · 2025
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsSarcopeniaOsteoarthritisWOMACCartilageBody mass indexBiomarkerPathophysiologyArticular cartilage

Abstract

fetched live from OpenAlex

Knee osteoarthritis (KOA) and sarcopenia are prevalent age-related disorders that share common pathophysiological mechanisms such as aging, chronic inflammation, and physical inactivity. Their coexistence may aggravate functional decline and disability. This cross-sectional study aimed to compare the prevalence of sarcopenia between individuals with KOA and matched controls and to explore the relationship between femoral intercondylar cartilage (FIC) thickness and muscle-related parameters. A total of 228 participants (114 KOA, 114 controls) matched by age, sex, and body mass index were enrolled. Assessments included appendicular skeletal muscle mass index (ASMMI), handgrip strength, walking speed, and physical activity. In KOA patients, ultrasound measurements of FIC and quadriceps thickness and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were additionally obtained. Sarcopenia prevalence was higher in the KOA group than in controls (41.2% vs. 26.3%, p = 0.017). Greater FIC thickness was associated with higher ASMMI, stronger handgrip strength, faster walking speed, and lower WOMAC pain and total scores. These findings indicate that FIC thickness may serve as a potential structural biomarker linking cartilage integrity with muscle function in KOA.

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)0.000
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 score0.011
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueLifeSame topicOsteoarthritis Treatment and MechanismsFrench-language works237,207