Evaluation of the effect of eye movements on the Sweep VEP
Bibliographic record
Abstract
Abstract Purpose To evaluate the effect of fixation stability on the sweep visual evoked potential (sVEP) visual acuity estimate in normal subjects. Methods Twenty eyes of 10 subjects with normal ophthalmic exam and best‐corrected Snellen chart visual acuity (BCVA) of 20/20 or better in the tested eye underwent sVEP visual acuity estimation using commercially available equipment and software (Diagnosys Espion E3 System Lowell, MA, USA). sVEP was sequentially recorded under two conditions: (i) steady fixation using a central stationary target; and (ii) simulated unsteady fixation with the subject allowed roving eye movements outside the central stationary target. Fixation was monitored using eye‐tracking technology (Tobii Eye Tracker 4C, Tobii Technology AB, Sweden). Fixation stability was classified based on the proportion of fixation points within 4° retinal diameter circle (stable fixation: >75% fixations within 4° circle; unstable fixation: <75% fixations within 4° circle). Within‐subject differences in the estimated sVEP visual acuities under the two tested conditions were assessed using Wilcoxon non‐parametric signed comparisons with significance level set at p < 0.05. Bland‐Altman plots were used to calculate 95% limits of agreement. Results Simulated unsteady fixation yielded significant differences in sVEP visual acuity estimation compared to steady fixation (p < 0.0001). The median difference between the two conditions was 0.19 logMAR (~2 Snellen VA lines), with 95% limits of agreement ranging from 0.05 to 0.49 logMAR. Steady fixation overestimated visual acuity compared to BCVA (p < 0.0001), contrary to simulated unsteady fixation, which yielded estimates not statistically different from the expected BCVA (p = 0.91). Conclusions Simulated unsteady fixation affects sVEP acuity estimates in normal subjects. However, the estimated visual acuities are not significantly different from the expected normal visual acuity of 20/20. sVEP testing can therefore be used to document normal visual acuity without the requirement of steady and central fixation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.001 | 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 teacher head, 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".