Comparing the effectiveness of a course of dynamic neuromuscular stabilization exercises and sensory-motor training on pain and trunk endurance in sciatica patients
Bibliographic record
Abstract
Background and Aims : Sciatica is a neuropathic disorder involving the nerve roots from the L3 vertebra downward. Its most prominent symptom is leg pain that radiates from below the knee down to the toes. The aim of the present study was to compare the effects of Dynamic Neuromuscular Stabilization (DNS) and Sensory-Motor Training (SMT) on pain and trunk endurance in patients with sciatica. Methods: This study was a semi-experimental field study conducted using a pre-test and post-test design. The study population consisted of 30 non-athletic women with sciatic nerve injury, selected through convenience sampling and randomly assigned to two groups of 15: a Sensory-Motor Training (SMT) group and a Dynamic Neuromuscular Stabilization (DNS) group. The Quebec Back Pain Disability Scale was used to assess pain intensity, and trunk flexion test was employed to evaluate lumbar endurance. The intervention included exercise sessions held three times per week, each lasting one hour, over a period of eight weeks. Data analysis was performed using analysis of covariance (ANCOVA), and a significance level of p ≤ 0.05 was applied to examine the effects of all variables. Results: The results showed that the Dynamic Neuromuscular Stabilization (DNS) group experienced a greater reduction in sciatic nerve pain in the post-test compared to the pre-test, relative to the Sensory-Motor Training (SMT) group. Conversely, the SMT group demonstrated a more significant improvement in trunk endurance from pre-test to post-test compared to the DNS group. Conclusion: Both training protocols can be effectively utilized as rehabilitation methods to improve the condition of individuals suffering from sciatic nerve pain.
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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) | 0.001 | 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) | 0.002 | 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".