Video Head Impulse Test for Early Diagnosis of Vestibular Neuritis Among Acute Vertigo
Bibliographic record
Abstract
BACKGROUND: This study assesses the value of the video head impulse test (vHIT) for early diagnosis of vestibular neuritis (VN) among acute vertigo. METHODS: Thirty-three cases of vestibular neuritis (VN), 96 patients with other acute vertigo (AV), and 50 cases of normal controls used vHIT to quantitatively test a pair of horizontal vestibulo-ocular reflection (VOR) gains, two pairs of vertical VOR gains, and the corresponding three pairs of VOR gain asymmetry. The peculiarity of VOR gains in VN and the differences between VN and other AV, normal controls by vHIT, were collected and analyzed. RESULTS: There were statistically significant differences in the three pairs of VOR gains asymmetry between VN and other AV, and normal controls (P<0.01). The sensitivity was 87.9% and specificity was 94.3% in differentiating VN from normal and other acute vertigo by vHIT. CONCLUSIONS: This study shows vHIT has advantages in the diagnosis of VN in acute vertigo with good sensitivity and specificity and indicates a widespread clinical application.
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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 | 0.000 |
| 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) | 0.001 | 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 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".