Vestibular evoked myogenic potential latencies in Meniere disease and vestibular schwannoma.
Bibliographic record
Abstract
OBJECTIVE: To evaluate vestibular evoked myogenic potentials (VEMPs) in Meniere disease and vestibular schwannoma. Given that the saccule and inferior vestibular nerve may be damaged in Meniere disease and vestibular schwannoma, respectively, VEMP latency may be prolonged in the patient's affected ear. DESIGN: Prospective study. SETTING: Urban otolaryngology practice. METHODS: Ten Meniere disease and 12 vestibular schwannoma patients. Subjects were tested with the VEMP head rotation protocol. MAIN OUTCOME MEASURE: VEMP latency. RESULTS: In Meniere disease patients, the pI latencies (mean +/- SEM, milliseconds) were 12.26 +/- 0.75 (healthy ear) and 14.20 +/- 0.73 (affected ear) (p = .041). The nI latencies were 20.29 +/- 1.06 (healthy ear) and 25.06 +/- 1.64 (affected ear) (p = .013). In vestibular schwannoma patients, the pI latencies were 12.02 +/- 0.93 (healthy ear) and 15.88 +/- 1.35 (affected ear) (p = .016). The nI latencies were 20.98 +/- 1.59 (healthy ear) and 24.84 +/- 1.08 (affected ear) (p = .031). CONCLUSION: VEMP pI and nI latencies were prolonged in the affected ear of Meniere disease and vestibular schwannoma patients. We propose classifying VEMP as abnormal if both the pI latency is > 1 ms longer and the nI latency is > 2 ms longer (sensitivity 66.7%, specificity 86.4%) compared with the other ear. This study suggests a role for VEMP in the clinical testing of these patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".