Virtual Reality in Stroke Rehabilitation: A Systematic Review of its Effectiveness for Upper Limb Motor Recovery
Bibliographic record
Abstract
PURPOSE: It is estimated that 50% to 75% of individuals who experience a stroke have persistent impairment of the affected upper limb (UL). There is a need to identify the best training strategies for retraining motor function of the UL. One intervention showing promise is virtual reality (VR), using either immersive or nonimmersive technology. Before recommending VR for use in clinical practice, it is important to understand the evidence regarding its effectiveness. METHOD: Two questions about the effectiveness of VR for UL rehabilitation in stroke were posed: (1) Is the use of immersive VR more effective than conventional therapy or no therapy in the rehabilitation of the UL in patients with hemiplegia? (2) Is the use of nonimmersive VR more effective than conventional therapy or no therapy in the rehabilitation of the UL in patients with hemiplegia? RESULTS: There is level 1b evidence suggesting an advantage to training in immersive VR environments versus no therapy in UL rehabilitation, and level 5 evidence for training in immersive VR versus conventional therapy. There is level 4 evidence showing conflicting results for training in nonimmersive VR versus no therapy, and level 2b evidence for training in nonimmersive VR versus conventional therapy. CONCLUSION: The current evidence on the effectiveness of using VR in the rehabilitation of the UL in patients with stroke is limited but sufficiently encouraging to justify additional clinical trials in this population.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".