Efficacy of McKenzie Manipulative Therapy on Pain, Functional Activity and Disability for Lumbar Disc Herniation
Bibliographic record
Abstract
Introduction: Lumbar disc herniation (LDH) is a common determinant of low back pain (LBP) and priority for cost-effective therapeutic approach is necessary. The objective of the study was to explore the effectiveness of McKenzie Manipulative Therapy (MMT) for patients with LDH. Methodology: This was an assessor blinded, 36-month RCT, at Center for Rehabilitation of the Paralyzed (CRP) in Savaar, Bangladesh. Seventy-two subjects, ages 28-47 years and clinically diagnosed with MRI findings of LDH, were randomly recruited from hospital records and sixty-eight found eligible. The control group received stretching exercise and graded oscillatory mobilization, and the experimental group received McKenzie manipulative therapy for 12 sessions in 4 weeks, both groups received a standard set of care also. The pain was the primary outcome and the secondary outcome was participation in functional activities and disability. Results: Pain and Disability found significant improvement in both groups, with the McKenzie approach significantly superior to the control group (p<.05). Bothersome in Activities (SBI) reported significantly lower post report compared to baseline for both groups (p<.01). McKenzie showed significantly superior outcomes for fear avoidance (FABQ) total and SBI feeling of abnormal sensation in leg compared to the control group (p <.05). Conclusion: The McKenzie manipulative therapy approach was found to be effective for pain, disability and participation in activities for single or multiple level LDH patients in a short time from day 1 to week 4, and the treatment effect extends after 6 months. Clinical Trial Registration No.: CTRI/2020/ 04/024667.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".