Does Sarcopenia Accompanying End-Stage Knee Osteoarthritis Affect the Outcomes following Total Knee Arthroplasty?
Bibliographic record
Abstract
Background and Objectives: This study aimed to investigate the prevalence of sarcopenia in patients undergoing total knee arthroplasty (TKA) for advanced knee osteoarthritis (OA), and to assess whether sarcopenia accompanying OA affects patient-reported outcome measures (PROMs) after TKA. We evaluated which predisposing factors could influence the development of sarcopenia in patients with advanced knee OA. Material and Methods: A total of 445 patients whose body composition, muscle strength, and physical performance could be measured before primary TKA were enrolled. Sarcopenia was defined according to the Asian Working Group for Sarcopenia 2019 criteria. Patients were categorized into sarcopenia (S, n = 42) and non-sarcopenia groups (NS, n = 403). PROMs were investigated using the Knee Injury and Osteoarthritis Outcome Score and Western Ontario and McMaster Universities Osteoarthritis Index. Additionally, postoperative complications and predisposing factors for sarcopenia were evaluated. Results: The incidence of sarcopenia in the entire sample was 9.4%; the prevalence was higher in men (15.4%) than in women (8.7%), and significantly increased with advancing age (p < 0.001). At the six–month follow-up, PROMs in group S were significantly inferior to those in group NS, except for the pain score; however, at the 12-month follow-up, no significant difference was observed between the groups. Multivariate logistic regression indicated that age, body mass index (BMI), and a higher modified Charlson Comorbidity Index (mCCI) were predisposing factors for sarcopenia. Conclusions: A higher prevalence of sarcopenia was observed in men with progressive knee OA. Up to six months after primary TKA, PROMs in group S were inferior to those in group NS, except for the pain score; however, no significant difference was observed between the groups at 12 months. Age, BMI, and higher mCCI were predisposing factors for sarcopenia in patients with OA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".