Bibliographic record
Abstract
OBJECTIVES: 1. To assess the reproducibility of eye movement velocity measurement using two methods: traditional electro-oculography (EOG) and infrared video-oculography (VOG) and, 2. Determine whether the normal values for unilateral weakness and bilateral reduction of caloric responses vary according to method employed. BACKGROUND: Vestibular testing frequently involves measurement of eye movements. EOG has been the standard method for decades, but VOG and other methods have recently become popular. The assumption has been that all methods measure eye movements equally and accurately but this assumption has not been validated. In this paper we examine this assumption. METHODS: Eye movements were recorded simultaneously with commercially available EOG and VOG methods to evaluate differences in results for nineteen normal subjects undergoing caloric tests with warm and cold water. Examination of the records permitted identification and simultaneous measurement of 840 nystagmus beats. RESULTS: EOG and VOG measurements were correlated but the correlation was not strong (Spearman rho = 0.529, p < 0.01). Eye velocities recorded by the VOG system were greater than that for the EOG system. The mean VOG/EOG ratio was 1.71. Normal values used at our centre were adjusted to accommodate the use of video technology to account for the differences in sensitivity between EOG and VOG methods. CONCLUSION: The traditional EOG-based normal value for bilateral reduction of caloric response, 30 degree per second (d/s) based on traditional EOG measurements should be revised to 50 d/s for modern VOG testing in our lab. Normal values for vestibular testing may need to be re-evaluated when new technology is introduced. Each lab should verify normal values for their own methods and equipment.
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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 | 0.035 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".