The efficacy of vestibular rehabilitation in patients with neurological disorders: a systematic review
Bibliographic record
Abstract
Objective: The aim of this systematic review is to critically assess the effectiveness of vestibular rehabilitation (VR) administered either alone or in combination with other neurorehabilitation strategies in patients with neurologic disorders. Data sources: An electronic search was conducted by 2 independent reviewers in the following databases: MEDLINE (PubMed), the Physiotherapy Evidence Database, and the Cochrane Database of Systematic Reviews. Study selection: All clinical studies carried out on adult patients with a diagnosis of neurologic disorders who performed VR provided alone or in combination with other therapies were included. Data extraction: Screening of titles, abstracts, and full texts and data extraction were undertaken independently by pairs of reviewers. Included studies were quality appraised using a modified version of the Newcastle-Ottawa Scale. Data synthesis: The summary of results was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Twelve studies were included in the review. All the included studies, with 1 exception, report that improvements provided by customized VR in subject affected by a central nervous system diseases are greater than traditional rehabilitation programs alone. Conclusions: Because of the lack of high-quality studies and heterogeneity of treatments protocols, clinical practice recommendations on the efficacy of VR cannot be made. Results show that VR programs are safe and could easily be implemented with standard neurorehabilitation protocols in patients affected by neurologic disorders. Hence, more high-quality randomized controlled trials of VR in patients with neurologic disorders 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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".