PROPRIOCEPTIVE NEUROMUSCULAR FACILITATION - AN INNOVATIVE APPROACH TO TREAT OSTEOARTHRITIS KNEE PATIENTS.
Bibliographic record
Abstract
Aim: Osteoarthritis (OA) knee is one of the major cause of mobility impairment, particularly amongpeople of age group 40 years and above .Though there are many treatments available for OA both Pharmacological and Non pharmacological, explore of PNF technique in OA knee is still in lacunae. To fillip the gap, the aim of this study is to find out the immediate effect of Proprioceptive Neuromuscular Facilitation (PNF) stretching in osteoarthritis knee patients. Methods: A prospective study was conducted at National Institute of Medical Science Superspeciality Hospital, Jaipur. This study included 1 00 patients and they were divided into Group A (n=50), which was control group and Group B (n=50) was experimental group.Group A and B under went out comemeasur ing tools like p ain measured using Numeric Pain Rating Scale (NPRS), Range of Motion ( R OM) of knee joint measured using Universal g oniometer and Functional activities of knee joint measured using short form Western Ontario and Mc Master Universities Osteoarthritis Index (WOMAC) before and after treatment. Group A patients receivedMoist Hot Pack (MHP) in knee joint for 5 minutes , where as Group B patients received MHP for 5 minutes and PNF stretching(Contract Relax - Antagonist Contract) for 3 min utes. The pre and post treatment data were recorded in Microsoft Excel and Student T- Test was used to evaluate clinical significance between groups. Results: It was found that Group B patients treated by PNF stretching improved a lot in OA knee symptoms with negligible effects in Group A patients treated by Moist hot pack. Conclusion: It was concluded from the study that PNF stretching may be a treatment option for patients with OA knee .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".