Manual Therapy Versus Closed Kinematic Exercises—The Influence on the Range of Movement in Patients with Knee Osteoarthritis: A Pilot Study
Bibliographic record
Abstract
Reduced range of motion is one of the main symptoms of knee osteoarthritis. These deficits are believed to have a negative impact on activities of daily living. The aim of the study was to examine how manual therapy and closed-chain kinematic exercises affect the range of motion in patients with knee osteoarthritis. Sixty-six patients with knee osteoarthritis were recruited and divided into three groups: manual therapy group, exercise group, and control group. The following parameters were evaluated before and after 10 days of rehabilitation: the range of motion in the open and closed kinematic chain using Orthyo sensors, pain intensity using Visual Analogue Scale (VAS), and the subjective functional assessment in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). The results indicate an increase in the range of motion after manual therapy in the open chain test and an increase in the possible range of motion in the closed chain test in the exercise group. Both study groups showed significant improvement of WOMAC-assessed function and a significant decrease of VAS-assessed pain following rehabilitation. Manual therapy and exercise affect the range of motion in patients with knee osteoarthritis. When examining the range of motion, it is worth taking into account various biomechanical conditions.
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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 | 0.000 | 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".