Virtual and augmented reality in the vestibular rehabilitation of peripheral vestibular disorders: systematic review and meta-analysis
Bibliographic record
Abstract
Vestibular rehabilitation therapy is an established treatment for patients with vestibular dysfunction. Virtual reality (VR) and augmented reality (AR) can be utilised in vestibular rehabilitation. Evidence of the efficacy of VR and AR delivered rehabilitation in patients with peripheral vestibular disorders is reviewed. MEDLINE, EMBASE, CENTRAL, CINAHL, PsychInfo, PsychBITE, OTSeeker, Ei Compendex, IEE, Clinical trials.gov and WebofScience databases were searched. Reduction in vestibular dysfunction symptoms 0-3 months post-intervention was the primary outcome. Secondary outcomes included long-term symptom improvement and side effects. Risk of bias assessment and meta analyses were planned. Five studies meeting eligibility criteria were included. Dizziness Handicap Inventory (DHI) scores 0-3 months post-intervention were reported by four studies. Meta-analysis identified a 1.13 (95% CI, - 1.74, - 0.52) standardized mean difference reduction in DHI in VR and AR treated patients compared to controls. Side effects reported by two studies were reduced by week four of VR intervention. Bias assessment identified DHI scores and side effects to be at high risk or of some concern. Adjunct VR interventions reduced patient DHI significantly more than vestibular rehabilitation alone 0-3 months post-intervention in adult patients diagnosed with unilateral vestibular disease. High quality studies are needed.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".