The effects of vestibular rehabilitation on balance in stroke:a systematic review and Meta-analysis
Bibliographic record
Abstract
Background: Vestibular Rehabilitation (VR) is a treatment to optimize vestibular function and sensory integration. It has demonstrated positive treatment effects in some neurological conditions. Objective: Patients after stroke often have balance and sensory impairments: yet there is no consensus on whether VR is useful in this population. This review assessed if VR can provide an effective treatment to optimize balance performance after stroke. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, four electronic databases were systematically searched for research studies comparing VR against routine care or controls in adults who had suffered a cerebral stroke in the last year. Study outcome data were collated and summarised narratively and a meta-analysis on balance outcomes was conducted using a random effects model. Results: Six randomised controlled trial studies met the inclusion criteria for this systematic review, with all being included in the meta-analysis. The pooled standardised mean difference (SMD) favoured the intervention as a beneficial treatment for balance recovery, with an effect of large magnitude (0.94; 95% confidence interval [CI] 0.39 to 1.48). No studies excessively influenced the primary outcome. No evidence for heterogeneity was revealed. Two studies showed low risk of bias, three some concerns and one high risk on the Cochrane Risk of Bias tool for randomised trials tool. Conclusions: Vestibular rehabilitation is beneficial for improving balance in stroke patients with mild-moderate balance dysfunction. Further research is needed on the application of VR in stroke patients to explore its clinical use in more detail.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.043 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".