Effects of Adding Motor Imagery to Early Physical Therapy in Patients with Knee Osteoarthritis who Had Received Total Knee Arthroplasty: A Randomized Clinical Trial
Bibliographic record
Abstract
OBJECTIVE: To investigate the effects of the inclusion of motor imagery (MI) principles into early physical therapy on pain, disability, pressure pain thresholds (PPTs), and range of motion in the early postsurgical phase after total knee arthroplasty (TKA). METHODS: A randomized clinical trial including patients with knee osteoarthritis who have received TKA was conducted. Participants were randomized to receive five treatment sessions of either physical therapy with or without MI principles in an early postsurgical phase after a TKA (five days after surgery). Pain intensity (visual analog scale [VAS], 0-100), pain-related disability (short-form Western Ontario McMaster Universities Osteoarthritis Index [WOMAC], 0-32), pressure pain thresholds (PPTs), and knee range of motion were assessed before and after five daily treatment sessions by an assessor blinded to the subject's condition. RESULTS: Twenty-four participants completed data collection and treatment. The adjusted analysis revealed significant group*time interactions for WOMAC (F = 17.29, P = 0.001, η2 = 0.48) and VAS (F = 14.56, P < 0.001, η2 = 0.45); patients receiving physiotherapy and MI principles experienced greater improvements in pain (Δ -28.0, 95% confidence interval [CI] = -43.0 to -13.0) and pain-related disability (Δ -6.0, 95% CI = -8.3 to -3.7) than those receiving physiotherapy alone. No significant group*time interactions for knee range of motion and PPTs were observed (all, P > 0.30). CONCLUSIONS: The application of MI to early physiotherapy was effective for improving pain and disability, but not range of motion or pressure pain sensitivity, in the early postsurgical phase after TKA in people with knee osteoarthritis.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".