Posturographic sensory ratios provide evidence for neuroplasticity after computerized vestibular rehabilitation therapy in a single group interventional trial
Bibliographic record
Abstract
BACKGROUND: Vestibular deficits are common and debilitating. Many patients do not achieve satisfactory resolution of their symptoms with standard rehabilitation techniques. This study seeks to measure changes in computerized dynamic posturography sensory ratio information after computerized vestibular retraining therapy (CVRT). METHODS: This prospective, single group, interventional study enrolled adult participants with stable, unilateral vestibular deficits. Before and after twelve twice weekly sessions of CVRT, and 4-6 and 10-12 months post-treatment, participants completed the Sensory Organization Test, from which sensory ratios (somatosensory - SOM, visual - VIS, vestibular - VEST, and visual preference - PREF) were calculated. RESULTS: 13 participants completed the intervention and post-retraining assessment; 9 completed the 4-6 and 10-12 month assessments. After CVRT, VIS increased by 11.6 (1.6 to 21.7) and VEST increased by 9.5 (0.6 to 18.3) and both remained significantly above baseline 10-12 months after treatment. The SOM and PREF ratios changed negligibly. Participants with mild disability (DHI ≤ 30) showed no change while participants with moderate-to-severe disability (DHI > 30) had significantly greater improvements in VIS (P = 0.0006) and VEST (P = 0.02) across all three post-treatment assessments. CONCLUSIONS: CVRT was associated with durable improvement in VIS and VEST sensory ratios and improved postural control under conditions that favour use of vestibular information, consistent with increased weighting of vestibular information over vision. TRIAL REGISTRATION: Clinicaltrials.gov registration NCT04875013; 04/27/2021.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".