Comparison of Maitland Mobilization and Mulligan Mobilization with Movement in Knee Osteoarthritis Patients
Bibliographic record
Abstract
Background: Osteoarthritis has no known cure. The management aims to decrease pain and improve the functional capacity and then the quality of life. Physiotherapy has played vital role to prevent all of these remedies for knee osteoarthritis that would be helping in economic burden as well as relieving symptoms of patients. Objective: The purpose of this study was to determine the outcome and comparative efficacy of Mulligans mobilization with movement and Maitland mobilization in the patients with knee osteoarthritis. Study type, settings & duration: A Quasi Experimental study was conducted at physiotherapy department of Kanaan Physiotherapy and Spine Clinic, Lahore from March to September 2020. Methodology: It was a Quasi Experimental study. Sample size was 56. Subjects were randomly distributed in to two groups with use of lottery method of randomization. Both male and female diagnosed knee osteoarthritis patients aged between 40-60 years were included. Patients having contraindication to mobilization, history of recent fractures were excluded. Group 1 patients got Maitland mobilization treatment and Group 2 got Mulligan mobilization with movement treatment. The goniometry, visual analog scale (VAS), knee range of motion (ROM), and Western Ontario McMaster OA (WOMAC) Index for knee osteoarthritis were used to assess all patients before and after 4 weeks of intervention. Statistical analysis was computed by SPSS version 21.0. Results: Pain decreased to greater extent in post treatment of Mulligan’s MWM group with mean value 2.64±1.13 as compared to 4.93±1.12 of Maitland Mobilization group. WOMAC Score increased to greater extent in post treatment of ............
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".