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Record W4312129571 · doi:10.3233/nre-220241

Durable improvement in participant-reported measures of disability and objective posturography after computerized vestibular retraining

2022· article· en· W4312129571 on OpenAlexaff
Eytan A. David, Navid Shahnaz

Bibliographic record

VenueNeurorehabilitation · 2022
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPosturographyVestibular systemMedicinePhysical therapyPhysical medicine and rehabilitationConfidence intervalRetrainingAudiologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Unilateral vestibular deficits are associated with postural instability and loss of quality of life. Common treatments frequently fail to achieve satisfactory outcomes. OBJECTIVE: To assess the durability of changes in participant-reported disability and objective posturography after computerized vestibular retraining. METHODS: This was a single-group study. Individuals with persistent symptoms of an objectively determined unilateral vestibular deficit completed questionnaires and posturography assessments before and after twelve sessions of computerized retraining, and 4-6 months and 10-12 months after treatment. RESULTS: 13 participants completed the post-treatment assessments; 9 completed the follow up. Mean improvements in perceived disability at 4-6 months after retraining were: DHI 14.3 points (95% confidence interval 4.0 to 24.5), ABC scale 14.9 points (4.3 to 25.6), FES-I 11.6 points (-3.2 to 26.5).The SOT composite score increased by 11.4 points (95% CI 1.9 to 20.9; p = 0.0175) immediately after treatment, 8.9 points (-2.9 to 20.7; p = 0.1528) at 4-6 months, and 10.6 points (2.2 to 19.0; p = 0.0162) after 10-12 months. At the 10-12 month time point, the areas of the functional stability region increased significantly for both endpoint excursion (p = 0.0086) and maximum excursion (p = 0.0025). CONCLUSION: Computerized vestibular retraining was associated with improved participant reported disability and objective measures of postural stability.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.271
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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