Effectiveness of Muscle Energy Technique and Proprioceptive Neuromuscular Facilitation in Knee Osteoarthritis
Bibliographic record
Abstract
This study was undertaken to determine the effectiveness of techniques such as muscle energy technique and proprioceptive neuromuscular facilitation in reduction of pain levels, improvement in flexibility of hamstring muscle and functional mobility, in patients with osteoarthritis of knee. An experimental study was carried out using a pre test and post test study design. The study was carried out in Krishna Hospital, Karad with a sample size of 36 individuals suffering from osteoarthritis of knee using a random sampling method. Individuals with Osteoarthritis of knee within the age group 40-60 years have been included. The collected data was analysed using paired and unpaired ‘t’ tests. When the data was analysed, the results showed that the patients in Group A and B receiving PNF stretching and MET respectively, the pain levels measured using NPRS shows more improvement in group A(7.66 ±1.02 ) as compared to group B (3.44 ±0.92). Hamstring flexibility in group A (10.63 ±4.89) was also found to be higher than in group B (4.37±2.01) when Active knee extension test was performed as a measure for hamstring flexibility, when functional mobility was measured using Western Ontario and McMaster universities arthritis index, improvement seen in group A (27.21 ±12.31) was significantly higher than in group B (14.11±7.88). The primary finding was that the patients of group A receiving PNF stretching along with the baseline protocol showed better improvement in pain levels, hamstring flexibility and level of functional mobility as compared to the patients in group B receiving MET along. with baseline protocol
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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 | 0.001 |
| 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) | 0.001 | 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".