The Impact of Immersive and Non-Immersive Virtual Reality Trends in Sensorimotor Recovery of Post-Stroke Patients-A Meta-Analysis
Bibliographic record
Abstract
Virtual Reality (VR) is an approach in stroke rehabilitation with ever-improving technological advancement for targeted motor rehabilitation by providing a user interface in a simulated environment with proprioceptive and visual feedback. This meta-analysis intended to evaluate the impact of immersive and non-immersive VR-based interventions compared to conventional rehabilitation in sensorimotor recovery following stroke. Randomized Controlled Trials based on the impact of VR, either immersive or non-immersive type in comparison to conventional rehabilitation on post-stroke patients (>18 years) sensorimotor recovery were searched on six databases including Google Scholar, PEDro, MEDLINE, Cochrane Library, EMBASE, and Web of Science from August to November 2020. A total of 17 randomized controlled trials on VR based intervention showed significant improvement in sensorimotor recovery following a stroke in overall FMA outcomes in comparison to the control group with pool effects in terms of SMD in a random effect model showed an impact of 0.498 at 95% CI (p<0.001) depicts a moderate effect size. An immersive and non-immersive emerging VR trend appears to be a promising therapeutic tool in sensorimotor recovery following stroke.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.044 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".