Associations Between Obesity With Low Muscle Mass and Physical Function in Patients With End-Stage Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To investigate the prevalence of obesity with low muscle mass and its impact on physical function, quality of life (QOL) and pain in patients with end-stage knee osteoarthritis over 65 years old. METHODS: In this cross-sectional study, we assessed a total of 562 patients. After separating the group into males and females, we divided patients into 4 further groups: normal BMI with normal muscle mass, obesity with normal muscle mass, normal BMI with low muscle mass and obesity with low muscle mass. All patients completed stair climbing test (SCT), 6-minute walk test, timed up and go test (TUG), instrumental gait analysis, Western Ontario McMaster Universities Osteoarthritis Index, VAS, and EuroQOL 5 dimensions questionnaire. RESULTS: Obesity with low muscle mass was diagnosed in 6 males subjects (7.8%) and 9 female subjects (1.9%). Patients with obesity and low muscle mass performed the SCT-ascent and descent significantly slower than other body composition groups in both males and females. TUG in males and gait speed in females were also significantly slower in the obesity with low muscle mass group. Stepwise multiple linear regression analysis revealed that in males, obesity with low muscle mass was significantly predictive of SCT ascent (β = 0.409, p < 0.001), SCT-descent (β = 0.405, p < 0.001), and TUG (β = 0.283, p = 0.009), and in females, obesity with low muscle mass was significantly predictive of SCT-ascent (β = 0.231, p < 0.001), SCT-descent (β = 0.183, p < 0.001), and gait speed (β=-0.129, p = 0.004). CONCLUSIONS: This study confirms that the combination of obesity and low muscle mass is associated with impaired physical function in patients with end-stage knee osteoarthritis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".