Virtual Reality and Noninvasive Brain Stimulation in Stroke: How Effective Is Their Combination for Upper Limb Motor Improvement?—A Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Efforts to augment post-stroke upper limb (UL) motor improvement include the use of newer interventions such as noninvasive brain stimulation (NIBS) and task practice in virtual reality environments (VEs). Despite increasing interest in using a combination of these 2 interventions, the effectiveness of this combination to enhance UL motor improvement outcomes has not been examined. OBJECTIVE: To evaluate the effectiveness of a combination of NIBS and task practice in a VE to augment post-stroke UL motor improvement. METHODS: We conducted a systematic search of the published literature using standard methodology. The Down and Black checklist and the Physiotherapy Evidence Database Research Organization Scale were used to assess study quality. We compared changes in UL impairment and activity levels between active stimulation and sham or other interventions using standardized mean differences and derived a summary effect size. RESULTS: We retrieved 5 studies that examined the role of a combination of NIBS and task practice in a VE to optimize UL motor improvement. These 5 studies included 3 randomized controlled trials, 1 cross-sectional study, and 1 crossover study. There was level 1a evidence that the combination was beneficial in subacute stroke. There was level 1b evidence that provision of real stimulation was not superior to sham stimulation in chronic stroke. Effect sizes favoring the combination were moderate for improvements in UL impairment and small for activity levels. CONCLUSIONS: Preliminary evidence supports the effectiveness of this combination in subacute stroke. Emergent questions need to be addressed to derive maximum benefit of this combination to augment post-stroke UL motor improvement. LEVEL OF EVIDENCE: I.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.055 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".