Impact of vestibular dysfunction on cognitive function
Bibliographic record
Abstract
ObjectiveTo investigate the impact of vestibular dysfunction on various domains of cognitive function, providing a basis for developing comprehensive vestibular-cognitive intervention strategies. MethodsA total of 33 patients with confirmed unilateral vestibular dysfunction treated at Eye & ENT Hospital, Fudan University between June 2024 and December 2024. Vestibular function was assessed using vestibular evoked myogenic potential (VEMP), caloric testing, video head impulse test (vHIT), and sensory organization test (SOT). Cognitive function was evaluated using mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA), Stroop color-word test, trail making test (TMT), and auditory verbal learning test (AVLT). Subjective symptoms were assessed using dizziness handicap inventory (DHI). ResultsIn the vestibular function assessment of patients, abnormalities in caloric testing, utricle VEMP and saccule VEMP results were most common, with rates of 87.9%, 57.6%, and 66.7%, respectively; SOT abnormality primarily characterized by impaired vestibular function (21.2%). Spearman correlation analysis showed age, years of education, hearing ability, and emotional state were associated with overall or specific domains of cognitive function in patients. Greater vestibular dysfunction severity was associated with longer TMT-A time (r＝0.443，P＝0.010), most severe damage of short-term (r＝－0.405，P＝0.019) and long-term delayed recalls (r＝－0.537，P＝0.001). Patients with 31-60 of DHI scores showed longer TMT-A time than patients with 0-30 of DHI scores (P＝0.033). ConclusionsPatients with vestibular dysfunction exhibit significant impairment in low-frequency semicircular canal and utricle function, which affects attention allocation, information processing speed, and memory performance in cognitive tasks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".