Effects of Elastic Resistance Exercise After Total Knee Replacement on Muscle Mass and Physical Function in Elderly Women With Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Knee osteoarthritis and age are associated with high sarcopenia risk, especially in patients who have received total knee replacement. The aim of this study was to identify the effects of elastic resistance exercise training after total knee replacement on muscle mass and physical outcomes in older women with knee osteoarthritis. DESIGN: Sixty older women who received unilateral primary total knee replacement surgery were randomized to an experimental group, which received 12 wks of postoperative elastic resistance exercise training, or a control group, which received standard care. The outcome measures included physical function performance (ie, Timed Up & Go, gait speed, forward reach, single-leg stance, timed chair rise), appendicular lean mass, and the Western Ontario and McMaster Universities Osteoarthritis Index. The assessment time points were 2 wks before surgery (T0), 1 mo after surgery (T1, before resistance exercise training), and 4 mos after surgery (T2, upon completion of resistance exercise training). RESULTS: After 12 wks of postoperative elastic resistance exercise training, the experimental group exhibited a significantly greater change in appendicular lean mass (mean difference = 0.81 kg, P = 0.004) than the control group. Elastic resistance exercise training also exerted significant effects on Timed Up & Go and gait speed with mean differences of 0.28 m/sec (P < 0.001) and -2.66 secs (P < 0.001), respectively. CONCLUSIONS: A 12-wk elastic resistance exercise training program after total knee replacement exerted benefits on muscle mass, mobility, and Western Ontario and McMaster Universities Osteoarthritis Index functional outcomes in older women with knee osteoarthritis.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".