Impact of sarcopenia on rehabilitation outcomes after total knee replacement in older adults with knee osteoarthritis
Bibliographic record
Abstract
Introduction:Knee osteoarthritis (KOA) is associated with an increased risk of sarcopenia, and aging-related muscle deterioration continues after total knee replacement (TKR). Low skeletal muscle mass index may influence postoperative rehabilitation outcomes. Through this study, we aimed to investigate the impact of preoperative sarcopenia on clinical outcomes after postoperative rehabilitation in older Asian adults.Methods:A total of 190 older adults (39 men, 151 women) were enrolled from two previous trials and were classified as having no sarcopenia, class I sarcopenia, or class II sarcopenia according to definitions provided by the Asian Working Group for Sarcopenia (AWGS) and the European Working Group on Sarcopenia in Older People (EWGSOP). All patients were retrospectively analyzed before (T0) and after (T1) TKR rehabilitation and 10 months after surgery (T2). The outcome measures included the timed up-and-go test (TUGT), gait speed (GS), timed chair rise (TCR), and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and physical difficulty (WOMAC-PF). With patient characteristics and T0 scores as covariates, an analysis of variance was performed to identify intergroup differences in changes of all outcome measures at T1 and T2.Results:According to the definitions of both the AWGS and EWGSOP, patients with class I and class II sarcopenia exhibited minor changes in TUGT, GS, TCR, and WOMAC-PF at T1 and T2 (all p 1 or T2 (all p > 0.05).Conclusions:Sarcopenia independently had negative impacts on the treatment effects of rehabilitation on physical mobility but not on pain outcome after TKR in older adults with KOA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".