The effect of open kinetic chain and closed kinetic chain exercises on dynamic balance and health status in elderly patients with osteoarthritis
Bibliographic record
Abstract
Background: Knee osteoarthritis is one of the most common musculoskeletal problems in the elderly, characterized by pain, stiffness, and decreased joint function, all of which negatively affect quality of life. Objective: To examine the effect of CKC and OKC exercises on dynamic balance and health status among elderly patients with knee osteoarthritis. Methods: This study employed a quasi-experimental design with a pretest-posttest two-group approach. The sample consisted of 30 elderly individuals aged ≥60 years with a medical diagnosis of knee osteoarthritis, selected using purposive sampling. Respondents were divided into two groups: 15 participants performed CKC exercises and 15 participants performed OKC exercises for two weeks with a frequency of three sessions per week. The Western Ontario and McMaster Osteoarthritis Index (WOMAC) was used to assess health status, and the Time Up and Go Test (TUG) was used to assess dynamic balance. Data were analyzed using paired t-tests with SPSS software. Results: There was a significant reduction in WOMAC scores in both the CKC group (38.47 ± 8.16 to 32.60 ± 9.43; p < 0.001) and the OKC group (37.53 ± 7.97 to 34.40 ± 8.40; p < 0.001). TUG performance also improved significantly in the CKC group (12.80 ± 1.08 to 11.20 ± 1.47; p < 0.001) and the OKC group (12.80 ± 1.21 to 11.27 ± 1.28; p < 0.001). Overall, improvements in health status were greater in the CKC group compared to the OKC group. Conclusion: Both CKC and OKC exercises are effective in improving dynamic balance and health status among elderly patients with knee osteoarthritis. CKC exercises tend to provide greater benefits in overall functional improvement.
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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) | 0.001 | 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".