Impact of Kinesiology Taping on Knee Osteoarthritis: Evaluating Effects on Pain, Stability, and Functional Performance in Patients
Bibliographic record
Abstract
Objective: This study investigates the impact of kinesiology taping (KT) on pain, joint stability, and functional performance in patients with knee osteoarthritis (OA). Methods: A randomized controlled trial was conducted with 50 participants diagnosed with knee OA, assigned to either KT (n=25) or a placebo taping group (n=25). KT was applied twice a week for 8 weeks. Outcomes were measured using the Visual Analog Scale (VAS) for pain, single-leg stance and functional reach tests for joint stability, and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and 6-minute walk test (6MWT) for functional performance. Results: The KT group demonstrated significant reductions in pain (VAS: 3.1 ± 1.0 vs. 5.2 ± 1.2, p < 0.001), improved joint stability (single-leg stance: 27.8 ± 6.3 seconds vs. 18.2 ± 5.6 seconds, p < 0.001; functional reach: 18.2 ± 4.2 cm vs. 14.7 ± 4.0 cm, p = 0.03), and enhanced functional performance (WOMAC score: 32.7 ± 11.8 vs. 45.3 ± 13.0, p = 0.02; 6MWT distance: 500.4 ± 60.2 meters vs. 440.3 ± 55.2 meters, p = 0.01) compared to the control group. Conclusion: KT effectively reduces pain, improves joint stability, and enhances functional performance in knee OA patients. These findings suggest that KT can be a valuable adjunctive treatment for managing knee OA symptoms.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".