A Neurotological Dilemma: Concurrent Management of Ménière’s Disease and Contralateral Vestibular Schwannoma
Bibliographic record
Abstract
Objective: To present the association of Ménière’s disease (MD) in one ear and vestibular schwannoma (VS) in the contralateral ear as a rare clinical entity, and discuss management options.Methods: A retrospective chart review of all patients diagnosed with MD over a ten year period was conducted for patients that met a definite diagnosis of MD in the ear contralateral to the ear with VS diagnosis, based on the 1995 MD criteria set by American Academy of Otolaryngology—Head and Neck Surgery.Results: Of 974 patients that met the inclusion criteria for a diagnosis of VS, five patients had a diagnosis of contralateral MD. The age range was 32-55 years. The size of VS ranged from 3-25 mm. All patients had the MD component managed with an aggressive medical protocol, and short courses of prednisone. For VS, four patients underwent surgery (three translabyrinthine, one retrosigmoid), and one patient opted for stereotactic radiation.Conclusion: The association of MD in one ear and VS in the contralateral ear represents a rare clinical scenario. Bilateral deafness and/or bilateral labyrinthine hypofunction are interventional risks that can lead to severe patient incapacitation. Tumor size, brainstem compression, tumor growth, residual functional hearing, and the degree of disability of MD vertiginous symptoms are important considerations in this management algorithm.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".