Oral Mycostatin as a possible alternative treatment for intractable Ménière's disease: preliminary cohort study
Bibliographic record
Abstract
Abstract Background: The potential efficacy of antifungal agents (e.g. Mycostatin) in treating acute attacks of Ménière's disease was first suggested in 1983 but few data have been published. Oral Mycostatin has been used as second-line medical treatment for intractable Ménière's disease at our institution for many years. Objective: This preliminary cohort study investigated the role of oral Mycostatin in intractable Ménière's disease. Methods: A retrospective review of patients with intractable Ménière's disease who started oral Mycostatin treatment between 2010 and 2012 was conducted. Results: Of 256 patients presenting with vertiginous disorders, 26 had definite Ménière's disease and had not responded to standard first-line treatment. Following oral Mycostatin treatment, improvements were reported for vertigo (n = 8), aural fullness (n = 7), tinnitus (n = 3) and subjective hearing loss (n = 3). Half of those with symptom improvement persisted with oral Mycostatin for two years and continued to remain asymptomatic. Conclusion: The use of oral Mycostatin to alleviate symptoms of intractable Ménière's disease showed promising results in this case series. Mycostatin may offer a safe and useful alternative for the management of Ménière's disease for patients with chronic unremitting symptoms in whom first-line treatment options have failed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".