Effect of Dynamic Balance Exercises Based on Visual Feedback on Physical Function, Balance Ability, and Depression in Women after Bilateral Total Knee Arthroplasty: A Randomized Controlled Trial
Bibliographic record
Abstract
The aim of this study was to compare the effects of dynamic balance exercises with and without visual feedback on recovery from total knee arthroplasty. The participants were 30 women who underwent total knee arthroplasty more than one week before the study, and were randomly allocated into two groups. The average ages of the experimental and control groups were 70.13 and 69.00 years, respectively. The dynamic balance exercise with visual feedback (VF) group practiced dynamic balance exercises using a laser pointer for five 30-min sessions over a 4-week period. The dynamic balance exercise without visual feedback (control) group practiced dynamic balance exercises for five 30 min sessions over a 4 week period. The following clinical measures were used for assessing physical function, balance ability, and depression. Compared with the control group, the VF group showed significant improvements in the physical performance test, Western Ontario and McMaster Universities Arthritis Index (WOMAC), confidence ellipse area (CEA), path length (PL), average velocity (AV), and Timed Up and Go test (TUG test) (p < 0.05). Furthermore, the VF group showed significant improvements in all post-surgery outcome measures compared with the pre-surgery values (p < 0.05). The above results indicated that the dynamic balance exercises based on visual feedback improved physical function and balance ability in patients following total knee arthroplasty, suggesting the need for effective rehabilitation programs for patients with total knee arthroplasty.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".