Clinic-Based Patellar Mobilization Therapy for Knee Osteoarthritis: A Randomized Clinical Trial
Bibliographic record
Abstract
PURPOSE: We performed a phase 2 randomized clinical trial to evaluate the preliminary effectiveness of a clinic-based patellar mobilization therapy (PMT) in patients with knee osteoarthritis. METHODS: We recruited 208 patients with knee osteoarthritis at primary care clinics in Hong Kong. Patients were randomly assigned (1:1) to the intervention group or the control group. The intervention group received 3 PMT treatment sessions from primary care physicians at 2-month intervals, with concomitant prescription of a home-based vastus medialis oblique muscle exercise. The control group received PMT after the study period. The primary outcome was the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score. Secondary outcomes included the WOMAC composite, function, and stiffness scores; the visual analog scale score for pain; objective physical function tests (30-s chair stand, 40-m walk test, timed up and go test, and EuroQol-5D). All outcomes were evaluated at baseline and at 24 weeks through intention-to-treat analysis. RESULTS: <.001). All secondary outcomes also demonstrated significant between-group differences. CONCLUSIONS: Patellar mobilization therapy has the potential to reduce pain and improve function and quality of life for patients with knee osteoarthritis. Future clinical trials with comparison to other active comparator controls will help determine the overall efficacy and facilitate the deployment of PMT in real-world practice.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".