Effectiveness of Maitland Mobilization versus Pain release phenomena for pain, range of motion and disability in early knee osteoarthritis
Bibliographic record
Abstract
Aim: To compare the effectiveness of Maitland mobilization and pain release phenomena for Pain, Range of motion disability in early knee osteoarthritis. Methodology: Randomized control trail was conducted at Department of Physical Therapy, Railway General Hospital, Rawalpindi, Pakistan within a duration of 6 months. Participants aged between 35-60 years including both genders, diagnosed with stage 1 and 2 knee osteoarthritis were included. Range of motion (ROM) was assessed by Goniometer, Numeric pain rating scale(NPRS) used for pain and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores used for knee disability. IBM SPSS 24 was used for statistical analysis. Non-parametric tests were used for NPRS while parametric test were used for knee ROM and knee disability . Results: Total 47 participants, were analyzed in which mean age in Experimental Maitland mobilization group A was 45.3±6.06 while mean age in experimental pain release phenomena group B was 45.4±4.59. Between-group analysis for NPRS was significantly improved at post intervention having p value (0.03). Between group comparison of knee ROM and WOMAC also showed significant p value. (p value=<0.05). Conclusion: It is concluded that both techniques are equally effective in decreasing pain, improving knee ROMs and functional mobility in early knee osteoarthritis. Keywords: Knee Osteoarthritis; Early Mobilization ; Knee Joint Pain; Knee Joint Range Of Motion; Knee Disability.
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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 | 0.001 |
| 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 | 0.000 |
| 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".