The Effect of Physiotherapy Manual Traction Techniques on the Western Ontario and McMaster University OA Index (WOMAC) in Knee Osteoarthritis Patients
Bibliographic record
Abstract
Age-related physical decline increases the risk of various diseases, including degenerative conditions, such as osteoarthritis (OA). OA is a chronic, progressive, multifactorial joint disorder characterized by cartilage deterioration, leading to chronic pain, stiffness, and impaired joint mobility. Knee OA significantly reduces patients' quality of life owing to functional limitations and pain. Manual traction therapy has shown potential in alleviating secondary inflammation in OA by lowering serum interleukin-1? levels and reducing pro-inflammatory cytokines and subchondral bone changes. This study aimed to assess the effect of manual traction physiotherapy on the Western Ontario and McMaster Universities Arthritis Index (WOMAC) in patients with knee OA. The study utilized a one-group pretest-post-test design, involving 36 participants selected through consecutive sampling from patients at the Sembiring Deli Tua Hospital Physiotherapy Clinic and Universitas Sumatera Utara's medical laboratory. Participants (aged 48–69 years, 80.6% female) underwent manual traction therapy twice a week for four weeks. The WOMAC questionnaire was used to evaluate changes in pain, stiffness, and physical function pre- and post-intervention. Statistical analysis using the Wilcoxon test revealed significant improvements across all WOMAC components, with pain scores decreasing from 14.81 to 7.58, stiffness scores from 4.0 to 2.5, and physical function scores from 30 to 23 (p < 0.001). These results demonstrate that manual traction therapy effectively reduces pain and stiffness while enhancing joint function in patients with knee OA.
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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) | 0.001 | 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".