Effectiveness of Virtual Reality Exercises in STroke <i>Rehabilitation</i> (EVREST): Rationale, Design, and Protocol of a Pilot Randomized Clinical Trial Assessing the Wii Gaming System
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that increasing intensity of rehabilitation results in better motor recovery. Limited evidence is available on the effectiveness of an interactive virtual reality gaming system for stroke rehabilitation. EVREST was designed to evaluate feasibility, safety and efficacy of using the Nintendo Wii gaming virtual reality (VRWii) technology to improve arm recovery in stroke patients. METHODS: Pilot randomized study comparing, VRWii versus recreational therapy (RT) in patients receiving standard rehabilitation within six months of stroke with a motor deficit of > or =3 on the Chedoke-McMaster Scale (arm). In this study we expect to randomize 20 patients. All participants (age 18-85) will receive customary rehabilitative treatment consistent of a standardized protocol (eight sessions, 60 min each, over a two-week period). OUTCOME MEASURES: The primary feasibility outcome is the total time receiving the intervention. The primary safety outcome is the proportion of patients experiencing intervention-related adverse events during the study period. Efficacy, a secondary outcome measure, will be measured by the Wolf Motor Function Test, Box and Block Test, and Stroke Impact Scale at the four-week follow-up visit. From November, 2008 to September, 2009 21 patients were randomized to VRWii or RT. Mean age, 61 (range 41-83) years. Mean time from stroke onset 25 (range 10-56) days. CONCLUSIONS: EVREST is the first randomized parallel controlled trial assessing the feasibility, safety, and efficacy of virtual reality using Wii gaming technology in stroke rehabilitation. The results of this study will serve as the basis for a larger multicentre trial. ClinicalTrials.gov registration# NTC692523.
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".