A New Proposal for Severity Evaluation of Menière's Disease by Using the Evidence From a Comprehensive Battery of Auditory and Vestibular Tests
Bibliographic record
Abstract
To date, no widely accepted criteria exist to quantify the severity of Menière's disease (MD) by using vestibular tests. This study aimed to compare association of hearing loss and vertigo severity with association of accurate assessments of vestibular function and the vertigo severity. The severity of vertigo was documented by a comprehensive medical history with number of vertigo attacks in the past 6 months and a Dizziness Handicap Inventory (DHI) questionnaire. The involvement of vestibular organs was verified by audio-vestibular tests in 80 definite MD patients. Correlations between DHI scores, number of vertigo attacks in the past 6 months, audio-vestibular function, and the number of involved vestibular end organs were evaluated. We show that there are no significant differences in either severity of vertigo or laboratory results across the degree of hearing loss. Furthermore, the number of involved vestibular end organs was significantly correlated with vestibulo-ocular reflex gain in anterior and posterior canal video head impulse test (vHIT), interaural asymmetry ratio in vestibular-evoked myogenic potentials (VEMPs), and number of vertigo attacks in the past 6 months and DHI score. The vestibulo-ocular reflex gain in the rotatory chair test (RCT) was significantly correlated with the DHI Physical scores and number of involved vestibular end organs at 0.08 Hz. These results indicate that hearing loss is a poor indicator of vertigo severity in MD whereas the number of involved vestibular end organs may serve as an objective measure for MD progress. A battery of vestibular tests targeting different sensor organs is a complementary method for evaluating inner ear deficits and may aid in "grading" the severity of MD.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".