Evidence for Vestibular Sensory Reweighting and Improvement in Dynamic Posturography after Computerized Vestibular Retraining for Stable Unilateral Vestibular Deficit
Bibliographic record
Abstract
Abstract Balance deficits increase the risk of falls and compromise quality of life. This single group, interventional study assesses a computerized vestibular retraining protocol in patients with objectively determined unilateral peripheral vestibular deficits. Participants received twelve twice-weekly sessions of vestibular retraining guided by an interactive display. Objective posturography tests and questionnaires were administered before and after retraining. We enrolled 13 participants (5 females and 8 males) with a median age of 51 years (range 18 to 67). After retraining, the median change in sensory organization test (SOT) composite scores was 8.8 (95% CI, 0.6 to 19.1). The SOT visual (median change of 0.12 [-0.09 to 0.30]) and vestibular (0.10 [-0.06 to 0.25]) ratios improved but there was no change in the somatosensory or visual preference ratios. Participants with moderate-to-severe disability at baseline (n=7), as measured by Dizziness Handicap Inventory, had a larger magnitude of improvement (SOT composite 14.6 [7.0 to 36.9]; visual ratio 0.16 [0.09 to 0.39]; vestibular ratio 0.12 [0.08 to 0.28]). We found that computerized vestibular retraining is associated with improvement in dynamic balance performance for individuals with stable unilateral vestibular deficits, consistent with sensory substitution to vestibular organs. Posturography improvements correlated with reduced perceived fall risk. Clinicaltrials.gov registration NCT04875013; 06/05/2021
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".