EFFECT OF KINESIOTAPING ON PAIN, RANGE OF MOTION, PHYSICAL PERFORMANCE AND FUNCTIONAL DISABILITY IN PATIENTS WITH KNEE OSTEOARTHRITIS- A RANDOMIZED CONTROL TRIAL
Bibliographic record
Abstract
Aim of Study: The aim of the study was to conduct a randomized control study to check the efciency of kinesiotaping in Knee Osteoarthritis patients. Material and Method: 30 subjects both male and female with unilateral / bilateral knee osteoarthritis were included in the study as per inclusion and exclusion criteria. Awritten informed consent was signed by the subjects in their own language. The subjects were assigned into two groups, Group (A) Experimental Group (n=15): received treatment with kinesiotaping and supervised exercise program. Group (B) Control Group (n=15): received treatment with supervised exercise program only. Outcomes were measured by Numerical Pain Rating Scale (NPRS), modied Western Ontario and McMaster universities OA index (m. WOMAC), Timed Up and Go test (TUG) and Goniometer. Appropriate statistical tool was applied. Result: The result showed that there was signicant (p<0.05) improvement in NPRS, m.WOMAC, TUG and ROM in both groups A & B after 6 weeks of treatment. But when compared between group A & B; group A showed signicant (p<0.05) improvement as compared to group B. Conclusion: The Kinesiotaping along with supervised exercise program showed statistically signicant improvement in reducing pain, functional disability increasing range of motion and physical performance as compared to the Supervised exercise program alone. Thus, Kinesiotaping along with supervised exercise program was more effective as compared to the supervised exercise program alone.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".