Does High‐Frequency Pseudo‐random Rotational Chair Testing Increase the Diagnostic Yield of the ENG Caloric Test in Detecting Bilateral Vestibular Loss in the Dizzy Patient?
Bibliographic record
Abstract
OBJECTIVES: To assess the incremental diagnostic yield of testing vestibulo-ocular (VOR) gain with high-frequency pseudo-random rotational chair (PsRRC) over testing with bithermal electronystagmography caloric tests in the dizzy patient, particularly in detecting bilateral vestibular loss. PATIENTS AND METHODS: One hundred ninety-eight patients presenting with dizziness underwent PsRRC and caloric testing. The VOR gain on PsRRC was measured at 0.32 to 5.0 Hz, with gain categorized as normal or decreased. PsRRC results were compared with caloric responses, also categorized as normal, or into graded categories of unilateral or bilateral vestibular loss. RESULTS: Reduced PsRRC gain was found in 29 (15%) patients, and reduced caloric tests responses in 70 (35%), with 25 (13%) having bilateral loss. Of patients with reduced chair gain, 25 of 29 (86%) demonstrated bilateral caloric loss. PsRRC gain was normal in most patients with unilateral caloric weakness, but was decreased in all patients with bilateral caloric weakness. The probability of a patient with completely normal caloric responses having an abnormal rotation chair in this study group was under 1% (1 of 128). CONCLUSIONS: PsRRC testing does not offer much additional diagnostic benefit when caloric responses are normal. It is useful in specific conditions, such as unilateral caloric loss for which the patient is not compensating, borderline caloric loss when traditional water caloric tests cannot be used, or for monitoring progressive bilateral vestibular loss.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".