The influence of visual vertigo and vestibulopathy on oculomotor responses
Bibliographic record
Abstract
OBJECTIVE: Dynamic visual inputs can cause visual vertigo (VV) in patients with vestibulopathy, leading to dizziness and falls. This study investigated the influence of VV on oculomotor responses. METHODS: In this cross-sectional, single-blind study, with experimental and control groups, 8 individuals with vestibulopathy and VV, 10 with vestibulopathy and no VV, and 10 healthy controls participated. Oculomotor responses were examined with 2-dimensional video-oculography. Participants were exposed to dynamic visual inputs of vertical stripes sweeping across a screen at 20 deg/sec, while seated or in Romberg stance, with and without a fixed target. Responses were quantified by optokinetic nystagmus frequency (OKNf) and gain (OKNg). RESULTS: Seated with no target, VV participants had higher OKNf than controls (37 ± 9 vs. 24 ± 9 peaks/sec; P < 0.05). In Romberg stance with no target, they had higher OKNf than controls (41 ± 9 vs. 28 ± 10 peaks/sec; P < 0.05). With a target, OKNf was higher in VV participants compared to controls (7 ± 7 vs. 1 μ 2 peaks/sec; P < 0.05). In Romberg with no target, OKNg was higher in the VV group (0.8 ± 0.1) compared to controls (0.6 ± 0.2; P=0.024). OKNf and OKNg did not differ according to VV status. CONCLUSIONS: VV participants had increased OKNf and OKNg compared to healthy participants. Visual dependency should be considered in vestibular rehabilitation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".