EFFECTIVENESS OF DRY NEEDLING WITH OR WITHOUT MULLIGAN TWO-LEG ROTATION TECHNIQUE ON PAIN, RANGE OF MOTION, AND JOINT DYSFUNCTION IN KNEE OSTEOARTHRITIS
Bibliographic record
Abstract
Background: Knee osteoarthritis (OA) is a prevalent degenerative joint condition affecting mobility, function, and quality of life, particularly in older adults. Conservative treatment options are frequently employed to manage symptoms and delay surgical intervention. Among these, dry needling and manual therapy techniques have shown promise in reducing pain and improving range of motion (ROM). However, evidence on their combined effects remains limited, necessitating further investigation to optimize therapeutic strategies. Objective: To evaluate and compare the effectiveness of dry needling alone versus dry needling combined with the Mulligan Two-Leg Rotation technique in reducing pain and improving joint function in individuals with knee osteoarthritis. Methods: This single-blinded randomized clinical trial included 44 participants aged 40 to 80 years, diagnosed with knee OA according to the Kellgren and Lawrence classification. Participants were randomly assigned into two equal groups (n=22). Group A received dry needling plus Mulligan Two-Leg Rotation, while Group B received dry needling alone. Both groups underwent four treatment sessions per week for eight weeks. Outcome measures included the Visual Analog Scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Universal Goniometer (UG), and Active Knee Extension (AKE) Test. Data were analyzed using SPSS version 24, with statistical significance set at p ≤ 0.05. Results: Post-treatment analysis revealed significantly greater improvements in Group A across all parameters. Group A showed a reduction in VAS scores from 8.75 ± 0.81 to 3.27 ± 1.48, while Group B improved from 8.75 ± 0.81 to 5.91 ± 1.12 (p = 0.000). WOMAC scores in Group A improved from 74.45 ± 2.80 to 25.23 ± 3.57, compared to 75.23 ± 2.89 to 44.50 ± 3.00 in Group B (p = 0.000). AKE and ROM also showed significantly higher gains in the intervention group (p < 0.001 for all comparisons). Conclusion: Dry needling combined with Mulligan’s Two-Leg Rotation is significantly more effective than dry needling alone in reducing pain, improving ROM, and enhancing functional outcomes in patients with knee OA. This combination should be considered a valuable component of non-pharmacological rehabilitation strategies for knee osteoarthritis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".