EFFECTIVENESS OF NEURAL MOBILIZATION AND STRETCHING EXERCISE FOR THE MANAGEMENT OF SCIATICA
Bibliographic record
Abstract
OBJECTIVE To find out the effectiveness of neural mobilization and stretching exercise for the management of sciatica BACKGROUND Sciatica is described as pain, radiating to the leg below the knee joint and caused by irritation of the sciatic nerve or nerve trunk. There are many treatment options for the management of sciatica, including stretching exercise and neural mobilization. MATERIAL AND METHODS This study was a Randomized Controlled Trial. 94 patients from physical therapy OPD of tertiary care hospitals, were participated in this study. Hence, 47 patients were randomly allocated into each group A and B. Before and after the nine treatment sessions, both groups were assessed with VAS, SLR ROM and Quebec back pain disability scale. In group A, neural mobilization with conventional therapy (heat and TENS) was applied, while stretching exercise with same conventional therapy was applied to group B. RESULTS It was observed that both treatments were helpful in reducing the symptoms. The analysis showed significant improvement (p-value <0.05) in the SLR to 60.851o±6.86oand Quebec score to 23.617±3.125, after the stretching exercise. Hence, both treatments were equally effective in reducing pain (p-value >0.05). CONCLUSION Stretching exercise is more effective in the improvement of SLR and disability. Furthermore, both techniques are helpful in the management of pain. KEY WORDS Sciatica, Stretching, Neural Mobilization, Straight Leg Raising, Visual Analogue Scale, TENS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".