Use of Virtual Reality Tools for Vestibular Disorders Rehabilitation: A Comprehensive Analysis
Bibliographic record
Abstract
Classical peripheral vestibular disorders rehabilitation is a long and costly process. While virtual reality settings have been repeatedly suggested to represent possible tools to help the rehabilitation process, no systematic study had been conducted so far. We systematically reviewed the current literature to analyze the published protocols documenting the use of virtual reality settings for peripheral vestibular disorders rehabilitation. There is an important diversity of settings and protocols involving virtual reality settings for the treatment of this pathology. Evaluation of the symptoms is often not standardized. However, our results unveil a clear effect of virtual reality settings-based rehabilitation of the patients' symptoms, assessed by objectives tools such as the DHI (mean decrease of 27 points), changing symptoms handicap perception from moderate to mild impact on life. Furthermore, we detected a relationship between the duration of the exposure to virtual reality environments and the magnitude of the therapeutic effects, suggesting that virtual reality treatments should last at least 150 minutes of cumulated exposure to ensure positive outcomes. Virtual reality offers a pleasant and safe environment for the patient. Future studies should standardize evaluation tools, document putative side effects further, compare virtual reality to conventional physical therapy, and evaluate economical costs/benefits of such strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".