Vestibular Migraine versus Méniere’s Disease: Diagnostic Utility of Electrocochleography
Bibliographic record
Abstract
Objectives: The diagnostic criteria for vestibular migraine (VM) and Méniere’s disease (MD) present an important overlap, which leads to a difficult diagnosis in patients presenting with headache, vertigo, hearing loss, ear fullness, and tinnitus. The objective of our study is to determine whether the area-under-the-curve ratio of the summating potentials (SP) and action potentials (AP) curves on electrocochleography (ECoG) helps differentiate VM from MD with or without the use of the well-established clinical criteria. Method: A retrospective review of patients filling either VM or MD criteria was undertaken between September 2015 and December 2018. All patients underwent ECoG before the introduction of anti-migraine therapy. The prediction of symptom improvement between the clinical criteria and ECoG results was compared by using the Vertigo Symptom Scale. Results: In total, 119 patients were included. An overlap of 36% exists between patients filling VM and MD criteria. Clinical criteria alone did not demonstrate a significant prediction of symptom response to anti-migraine therapy (VM 83%, MD 51%; p = 0.10). However, ECoG results alone did demonstrate adequate prediction (VM 94%, MD 32%; p < 0.001). A negative ECoG result combined with the clinical criteria of VM (100% symptom improvement) was shown to be more predictive of treatment response when compared to clinical criteria alone (83% symptom improvement) (p = 0.017). Finally, when used in patients filling both the VM and MD criteria (VMMD), ECoG was able to predict symptom improvement, thus better differentiating both diseases (normal ECoG: 95%, abnormal ECoG 29%; p < 0.001). Conclusion: Combining VM criteria with normal ECoG using the AUC ratio seems superior in predicting adequate symptom improvement than VM criteria alone.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".