Bibliographic record
Abstract
PURPOSE: Balance exercise as well as lower extremity strengthening exercise (LESE) is known to be effective in patients with knee osteoarthritis (KOA). The purpose of this study was to investigate the effectiveness of performing LESE in conjunction with balance exercise on lower extremity function, range of motion, muscle strength, and balance in patients with KOA. METHODS: The subjects of this study were 25 patients with KOA who were recruited and randomly divided into two groups: 1) those who performed LESE with balance exercise; and 2) those who performed only LESE. Both the groups also received general physical therapy and performed aerobic exercise. The interventions were performed 3 times a week for 4 weeks. To determine the effectiveness of the interventions, we measured Western Ontario and MacMaster Universities Arthritis Index (WOMAC) score, numerical rating scale (NRS) score, passive range of motion (PROM), chair stand test (CST), and Berg Balance Scale (BBS) score at the initiation of the interventions and again after 4 weeks, at the time of completion of the interventions. RESULTS: After 4 weeks of the interventions, both the groups showed significantly improved WOMAC (p<.01), NRS (p<.01), PROM (p<.05), CST (p<.05), and BBS (p<.01) scores. However, there was no significant difference between the groups in terms of the clinical outcomes observed. CONCLUSION: These results suggest that the addition of balance exercise to a LESE regimen in patients with KOA did not provide any additional benefit.
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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 | 0.000 |
| 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) | 0.003 | 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".