STUDY ON EFFECTIVENESS OF PROPRIOCEPTIVE NEUROMUSCULAR FACILITATION (PNF) TECHNIQUE AND CONVENTIONAL THERAPY IN TREATING THE PATIENTS WITH CERVICAL SPONDYLOSIS
Bibliographic record
Abstract
Background:Proprioceptive Neuromuscular Facilitation (PNF)A wide range of treatment techniques and approaches from different philosophical backgrounds are utilized in Neurological RehabilitationThe aim of this study is to evaluate the effectiveness of proprioceptive neuromuscular facilitation techniqueand conventional therapy in treating the patients with Cervical Spondylosis by improving the pain and range of motion of neck.Subjects and methods: 40 cervical Spondylosis patients of both the sexes selected on the basis of inclusion and exclusion criteria wereincluded in the study and randomly divided into two groups A and B each of 20 persons.Group A consisting of 10 males and 10 females received PNF (Hold -relax and Contract relax) exercise for 4 weeks, 5 days/ week once in a day and Group B consisting of 11males and 9 females received conventional therapy for 4 weeks, 5 days/ week once in a day.Variables are measured pre intervention and post intervention after 4 weeks.To evaluate changes in pain, a shortened version of the McGill Questionnaire was used ,Range of motion is measured.Result:Group A shows more significant improvement in all variables (Pain, ROM) in cervical spondylosis subjects than Group B.Conclusion:Analysis of the results confirmed that both PNF and conventional therapy had a statistically significant impact on reducing pain and improving the range of motion of neck in subjects suffering from spondylosis, but PNF method proved to be more effective than conventional therapy and McGill score of PNF applied group were more significant.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".