The High-Frequency/Acceleration Head Heave Test in Detecting Otolith Diseases
Bibliographic record
Abstract
OBJECTIVE: To investigate whether transient, high-acceleration interaural head heaves (translational vestibulo-ocular reflex [tVOR]) could aid in the diagnosis of otolith diseases. STUDY DESIGN: Prospective cohort study. SETTING: Tertiary referral center. PATIENTS: Thirteen patients with symptoms suggestive of otolith diseases and 10 age-matched controls. INTERVENTIONS: Patients underwent a clinical otoneurologic examination and standard laboratory audiovestibular evaluation, including audiometry, electronystagmography with bithermal caloric, Halmagyi-Curthoys head thrust test with search coils, and vestibular-evoked myogenic potential. All subjects underwent subjective visual vertical (SVV) and tVOR testings. MAIN OUTCOME MEASURES: Sensitivity (ratio of peak eye to peak head velocities) and velocity gain (ratio of actual to ideal peak eye velocities). RESULTS: Five of 13 patients showed no abnormality in any tests. Of the remaining 8, 3 (38%) had reduced tVOR responses, whereas 1 (13%) had abnormal SVV. Sensitivity and velocity gains were symmetrically reduced in 2 patients, who had symptoms for 8 and 24 months. A third patient, symptomatic for 7 weeks, had asymmetric reduction of tVOR responses and a deviated SVV. CONCLUSION: Both head heave and SVV tests detect acute, asymmetric otolith diseases. Subjective visual vertical test relies on imbalance of utricular tone and may not detect bilateral symmetric diseases or partial diseases with central compensation. Our preliminary data in a small group of patients show that measuring the tVOR in a higher and more physiologic range of frequencies may serve as useful adjunct to detect acute and chronic otolith dysfunction and seems to be superior to the SVV in detecting bilateral symmetric or asymmetric otolith diseases.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".