Galvanic vestibular stimulation for postural rehabilitation in neurological disorders: a systematic review
Bibliographic record
Abstract
Background: Galvanic vestibular stimulation (GVS) may potentially improve postural rehabilitation. However, the postural control role of GVS in the neurological disorders has not been systematically reviewed. Methods: We conducted a systematic review on PubMed, EMBASE, and Web of Science to synthesize key findings of the effectiveness of single and multiple sessions of GVS alone and combined with other interventions on balance in adults with neurological disorders. Diagnosis of neurological disorders, sample size, age and gender of participants, GVS parameters, postural assessments, and study findings were extracted following the PRISMA guidelines. Newcastle-Ottawa scale was used to assess study quality. Results: Twenty-five studies were included in the systematic review. Clinical application of GVS for postural control included Parkinson's disease, bilateral vestibulopathy, stroke-induced hemiplegia, multiple sclerosis, progressive supranuclear palsy, persistent postural-perceptual dizziness, and unilateral vestibulopathy. GVS effectively improves postural control in most neurological disorders. Risk of bias assessment showed that most studies had a low risk of bias. Conclusion: GVS is a promising complementary therapy to improve postural control and balance in adults with neurological disorders. Future high quality studies should be performed to confirm these findings.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".