Virtual Reality in Improving Anticipatory Postural Adjustments to Step Initiation in Individuals with Knee Osteoarthritis: A Randomized Controlled Trial
Bibliographic record
Abstract
Background:Virtual reality (VR) has been suggested as a new therapeutic approach in various sectors of rehabilitation, including the treatment of patients with knee osteoarthritis (OA), and one of its treatment goals is to improve the gait pattern and walking ability of patients. Objective:This study aimed to evaluate if VR, along with conventional physiotherapy treatment (CT), has superior effects to kinesiotherapy alone on pain, physical capacity, balance, and the parameters of anticipatory postural adjustments (APAs) in patients with knee OA. Participants:Forty participants (31 women and 9 men) with knee OA in at least one knee and able to ambulate independently. Intervention:A rehabilitation program (8 consecutive weeks, 50-minute session, twice a week). Patients were randomized into the intervention groups CT or VR. Main Outcome Measures:Primary—latency of APA, amplitude of APA, and time to reach the maximum acceleration amplitude. Secondary—balance control by Mini-Balance Evaluation Systems Test, pain, and physical capacity by Western Ontario and McMaster Universities Arthritis Index. Results:The results of the study showed that conventional treatment significantly improved pain intensity, physical capacity, and balance in individuals with knee OA; however, only the group that used VR showed improvement in the APA parameters. Conclusion:This study demonstrated that VR associated with conventional treatment improved APAs 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| 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 | 0.003 | 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".