Vection in Patients with Glaucoma
Bibliographic record
Abstract
PURPOSE: Large moving scenes can induce a sensation of self-motion in stationary observers. This illusion is called "vection." Glaucoma progressively affects the functioning of peripheral vision, which plays an important role in inducing vection. It is still not known whether vection can be induced in these patients and, if it can, whether the interaction between visual and vestibular inputs is solved appropriately. The aim of this study was to investigate vection responses in patients with mild to moderate open-angle glaucoma. METHODS: Fifteen patients with mild to moderate glaucoma and 15 age-matched controls were exposed to a random-dot pattern at a short viewing distance and in a dark room. The pattern was projected on a large screen and rotated clockwise with an angular speed of 45 degrees per second to induce a sensation of self-rotation. Vection latency, vection duration, and objective and subjective measures of tilt were obtained in three viewing conditions (binocular, and monocular with each eye). Each condition lasted 2 minutes. RESULTS: Patients with glaucoma had longer vection latencies (p = 0.005) than, but the same vection duration as, age-matched controls. Viewing condition did not affect vection responses for either group. The control group estimated the tilt angle as being significantly larger than the actual maximum tilt angle measured with the tilt sensor (p = 0.038). There was no relationship between vection measures and visual field sensitivity for the glaucoma group. CONCLUSIONS: These findings suggest that, despite an altered visual input that delays vection, the neural responses involved in canceling the illusion of self-motion remain intact in patients with mild peripheral visual field 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 | 0.000 | 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) | 0.003 | 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".