Cognitive function in individuals with different peripheral vestibular disorders: a comparative study
Bibliographic record
Abstract
Abstract Introduction The vestibular neural network is connected to cortical and subcortical areas of the brain suggesting the possible interconnection between vestibular and cognitive functions. The study examines the impact of peripheral vestibular disorders like Benign Paroxysmal Positional Vertigo (BPPV), Meniere’s Disease (MD) and Acute Vestibular Neuropathy (AVN) on cognitive skills and its related aspects. The study also explored if there is any relationship between dizziness related to self-perceived handicap and cognitive skills among individuals with vestibular disorders. A cross-sectional study design was employed to assess cognitive skills using Montreal Cognitive Assessment (MoCA) and Digit span test (DST) in 34 individuals with peripheral vestibular dysfunction and 34 healthy controls. Dizziness Handicap Inventory (DHI) was administered to assess the self-perceived dizziness related handicap. The relationship between the self-reported dizziness related handicap through Dizziness Handicap inventory (DHI) and performance-based cognition tests was assessed. Results Mann Whitney U test indicated significantly poorer scores on various cognitive domains of MoCA test and DST in individuals with vestibular dysfunction than healthy individuals. Comparison of the cognitive skills across individuals with BPPV, MD and AVN revealed no significant difference among them. There was no correlation observed between self-perceived dizziness handicap and cognitive abilities in individuals with vestibular dysfunction. Conclusion The study revealed that cognitive dysfunction is affected in individuals with peripheral vestibular loss, specifically in domains like visuospatial skills, executive functioning, memory, attention and language. However, cognition need not always linked to perceptual handicap reported by these individuals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".