Morphometric Evaluation of Facial and Vestibulocochlear Nerves Using Magnetic Resonance Imaging in Patients with Menière’s Disease
Bibliographic record
Abstract
Abstract Several studies proposed a loss of neural structures (such as hair cells or neurons within the spiral ganglion) in Menière’s disease (MD). It has been shown that VIIth and VIIIth cranial nerves are enlarged within MD patients compared to normal controls. We now aimed to investigate potential differences in these two nerves in patients with MD. Etiology of endolymphatic hydrops, central pathological hallmark of MD, includes genetic predisposition, autoimmune processes, viral infections, cellular apoptosis and oxidative stress. We evaluated morphometric properties (long and short diameter, cross-sectional area) of the VIIth and VIIIth cranial nerves passing from the cerebellopontine angle to the inner ear modiolus, acquired on a clinical 3T magnetic resonance imaging scanner. 71 patients with MD were included, 53 of whom clinically showed a unilateral affection. Our data showed no differences in nerve morphometry between the clinically non-affected and the clinically affected side in patients with clinically unilateral MD. There was also no correlation to duration of symptoms, in contrast to previously demonstrated correlations between clinical features and the extent of endolymphatic hydrops. A disease process starting before the onset of clinical symptoms could be a potential explanation.
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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) | 0.002 | 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".