Vestibular Rehabilitation Using Dynamic Posturography: Objective and Patient‐Reported Outcomes from a Randomized Trial
Bibliographic record
Abstract
OBJECTIVE: Balance deficits are common and debilitating. Standard treatments have limitations in addressing symptoms and restoring dynamic balance function. This study compares a rehabilitative computerized dynamic posturography (CDP) protocol, computerized vestibular retraining therapy (CVRT), with a home exercise program (HEP) for patients with objectively confirmed unilateral vestibular deficits (UVDs). STUDY DESIGN: Single-center, randomized, interventional trial, with 1-sided crossover. SETTING: A tertiary neurotology clinic. METHODS: Patients with UVDs and Dizziness Handicap Inventory (DHI) score >30 were randomized to receive either CVRT or HEP. After completion of treatment, the HEP group was crossed over to CVRT. Outcome measures were the sensory organization test (SOT) and 3 participants reported dizziness disability measures: the DHI, Activity-Specific Balance Confidence Scale (ABC) scale, and Falls Efficacy Score-International (FES-I). RESULTS: We enrolled 37 patients: 18 participants completed CVRT and 12 completed HEP, 11 of whom completed the crossover. Seven participants withdrew. The CVRT group demonstrated a greater improvement in SOT composite score than the HEP group (P = .04). Both groups demonstrated improvement in participant-reported measures but there were no differences between groups (DHI: P = .2604; ABC: P = .3627; FES-I: P = .96). Following crossover to CVRT after HEP, SOT composite (P = .002), DHI (P = .03), and ABC (P = .006) improved compared to HEP alone. CONCLUSION: CVRT and HEP were both associated with improved participant-reported disability outcomes. CVRT was associated with greater improvement in objective balance than HEP. Adding CVRT after HEP was superior to HEP alone. Multimodal CDP-based interventions, such as CVRT, should be considered as an adjunct to vestibular physiotherapy for patients with UVD.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".