A Comparative Study on the Effectiveness of Rhythmic Stabilization Technique (PNF) and Conventional Physiotherapy in the Treatment of Knee Osteoarthritis
Bibliographic record
Abstract
Osteoarthritis (OA) of the knee presents with pain, joint difficulty, reduced range of motion (ROM), and impaired proprioceptive accuracy, impacting the quality of life for affected individuals. Proprioceptive Neuromuscular Facilitation (PNF) stretching techniques have emerged as interventions to address muscle flexibility, pain, and ROM in OA. Recent research has highlighted impaired proprioceptive accuracy as a contributing factor in the onset and progression of knee OA. Rhythmic stabilization, a component of PNF, targets mechanoreceptor stimulation, offering promise in alleviating OA symptoms. This study aimed to compare the effectiveness of rhythmic stabilization PNF with conventional physiotherapy in managing knee OA. Thirty-six participants aged 40-60 were randomly assigned to two groups: Group A received rhythmic stabilization PNF, while Group B received conventional physiotherapy. Outcome measures included the Western Ontario and McMaster Universities Arthritis Index (WOMAC) and visual analog scale (VAS) for pain assessment. Results indicated significant pain reduction, increased flexibility, and improved functional mobility in both groups post-treatment. However, the rhythmic stabilization PNF group demonstrated superior outcomes compared to conventional physiotherapy alone. This suggests that rhythmic stabilization PNF is more effective in reducing pain, enhancing flexibility, and promoting functional independence in Grade 2-3 knee OA. These findings underscore the potential of incorporating rhythmic stabilization PNF into OA management protocols to optimize patient outcomes
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".