Vestibular Rehabilitation Using Dynamic Posturography: Functional Stability and Fall Risk Outcomes From a Randomized Trial
Bibliographic record
Abstract
OBJECTIVE: To compare computerized vestibular retraining therapy (CVRT) to a home exercise program (HEP) for the treatment of unilateral vestibular deficits. STUDY DESIGN: Randomized, unblinded, interventional study with single crossover. SETTING: This study was performed in a tertiary neurotology clinic. METHODS: Individuals with a stable unilateral vestibular deficit present for greater than 6 months, confirmed with videonystagmography and vestibular evoked myogenic potential testing and scoring >30 on the dizziness handicap inventory, received either 12 twice-weekly sessions of CVRT or 6 weeks of HEP. Outcome measures were the limits of stability test with submeasures: reaction time; directional control; movement velocity; endpoint and maximum excursion; endpoint and maximum functional stability region. RESULTS: CVRT (n = 18), but not HEP (n = 12), was associated with improvement in all measures and with fewer instances of loss of balance during testing. CVRT was superior to HEP for directional control (24.0; 95% CI 4.4-43.6; P = .01), movement velocity (1.5; 95% CI 0.6-2.3; P < .001), and endpoint excursion (21.1; 95% CI 4.8-37.4; P < .01). Improvements in directional control (23.0; 95% CI 1.1-45.0; P = .046) and movement velocity (1.3; 95% CI 0.4-2.2; P = .04) were greater for HEP/CVRT crossover than for HEP alone. There were no adverse effects of either treatment. CONCLUSION: CVRT improved postural stability in the limits of stability test. CVRT was associated with greater improvement than HEP in lean angle, accuracy, and speed of volitional leaning. TRIAL REGISTRATION: Clinicaltrials.gov NCT05115032 (https://clinicaltrials.gov/study/NCT05115032).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".