Preliminary report: Neural firing patterns specific for Meniere's disease
Bibliographic record
Abstract
OBJECTIVE: To describe the application of a new, objective diagnostic test for Meniere's disease. INTRODUCTION: Electrovestibulography (EVestG) is a complex, newly-developed test paradigm that searches for neural firing patterns that may be diagnostic for particular neural disorders. EVestG system was previously "trained" to distinguish Meniere's disease from other patients on a set of training data. In this paper we illustrate its diagnostic application in a new group of unknown subjects. SETTING: Collaborative Academic Bioengineering Research Centre. STUDY DESIGN: Prospective, blinded human Clinical Trial. METHODS: In an attempt to understand the specific neural firing patterns that may objectively characterize latent Meniere's disease, two hundred fifty-six consecutive patients who presented for electronystagmography testing were asked to undergo EVestG testing. Ten subjects actually completed testing but data were too noisy to permit analysis for one patient. Complete data were available for nine patients with either a clinical diagnosis of either Meniere's disease (4 patients) or some other vestibular disorder (2 vestibular neuritis, 2 benign positional vertigo and 1 non-specific dizziness). None of the patients were experiencing attacks of vertigo within a week of EVestG testing. Ten normal control subjects with no history or symptoms of ear disease were also tested. EVestG was performed in a separate engineering research facility by investigators who were unaware of their clinical diagnosis. If EVestG suggested that the probability of Meniere's disease was 0.5 or greater Meniere's disease was considered present by the objective testing. The objective and clinical diagnoses were compared. RESULTS: EVestG testing correctly identified three of four Meniere's disease patients and rejected the diagnosis in 9 of the 10 controls. Two of the 5 dizzy, non-Meniere's patients were incorrectly identified as Meniere's disease. The sensitivity and specificity of EvestG testing were 75% and 80%, respectively. EVestG results were statistically significantly different for Meniere's patients versus the other dizzy patients and controls (Univariate ANOVA difference contrasts p = 0.0340) even in this small sample. CONCLUSION: The EVestG protocol appeared to show promise as an objective, diagnostic test for Meniere's disease, but our sample size is too small to generalize widely. LEVEL OF EVIDENCE: N.A. Prospective Human clinical trial.
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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 | 0.000 | 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) | 0.004 | 0.001 |
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".