Morphometric Evaluation of the Facial and Vestibulocochlear Nerves Using MR Imaging in Patients with Menière Disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Menière disease (MD) is a condition of unknown etiology, involving genetic predisposition, autoimmune processes, viral infections, cellular apoptosis, and oxidative stress. This study aimed to investigate potential differences in cranial nerves VII and VIII in patients with MD using hydrops MRI (FLAIR) for morphometric evaluations. MATERIALS AND METHODS: Sequences acquired were 3T MRI, CISS, and 3D FLAIR. We evaluated the morphometrics of cranial nerves VII and VIII from the cerebellopontine angle to the internal auditory canal fundus, comparing the nonaffected and affected sides. Furthermore, we examined the findings in relation to symptom duration and evaluated the feasibility of FLAIR in the morphometry of the cranial nerves. RESULTS: A total of 53 patients with MD with unilateral symptoms were included. After statistical analysis, no significant differences were found regarding morphometric changes in the affected side compared with the nonaffected side of cranial nerves VII and VIII. There was also no significant difference between the morphometric evaluations of patients with different symptom durations. The morphometric evaluation using hydrops MRI sequences (FLAIR) showed no significant difference compared with established morphometric highly T2-weighted imaging (CISS). CONCLUSIONS: Our data found no differences in nerve morphometry between clinically nonaffected and affected sides in patients with unilateral MD, nor any correlation with symptom duration. This finding contrasts with previous ones of correlations between clinical features and endolymphatic hydrops. A disease process starting before clinical symptom onset could be a possible explanation. Morphometric evaluation of brain nerves using hydrops MRI sequences is practical and provides similar results compared with T2-weighted imaging, improving patient comfort and reducing MRI scan times.
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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 | 0.001 | 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) | 0.001 | 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".