Effects of Exercise Training Alone and in Combination With Kinesio Taping on Pain, Functionality, and Biomarkers Related to the Cartilage Metabolism in Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To investigate the effects of exercise training alone and in combination with kinesio taping on pain, functionality, and circulating cartilage oligomeric matrix protein (COMP), and matrix metalloproteinase (MMP)-1, and -3 at rest and immediately after walking exercise in knee osteoarthritis (OA). DESIGN: A total of 22 female patients diagnosed with knee OA were randomly divided into the exercise training (ET) or exercise training plus kinesio taping (ET + KT) groups. The patients in the ET performed exercise training for 6 weeks. The patients in the ET + KT group were applied with kinesio tape in addition to the exercise training for 6 weeks. In both groups, 20 minutes of walking exercise were performed before and after the interventions. The pain and functional status of the patients were assessed using visual analogue scale (VAS) and Western Ontario McMasters Osteoarthritis Index (WOMAC) before and after the intervention at rest, respectively. Blood samples were taken at rest and immediately after the walking exercise before and after the interventions for the analysis of COMP, MMP-1, and MMP-3 levels. RESULTS: < 0.05). CONCLUSIONS: Exercise training and exercise training plus kinesio taping improved pain and physical function; however, the COMP, MMP-1, and MMP-3 levels did not change.
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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) | 0.001 | 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) | 0.002 | 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".