Fixation Patterns in Maculopathy
Bibliographic record
Abstract
PURPOSE: The goal of this study was to explore binocular coordination during fixation in patients with age-related macular degeneration (AMD) and to investigate whether there is a shift in eye position when the viewing condition changes from binocular to monocular. METHODS: Sixteen people with normal vision and 12 patients with AMD were asked to look at a 3 deg fixation target with both eyes and with each eye individually while the fellow eye was covered by an infrared filter. Fixational eye movements were recorded for both eyes with an EyeLink eye-tracker in all conditions. The shift in eye position at the end of every fixation period was calculated for each eye. RESULTS: All people with normal vision as well as the majority of patients had good binocular coordination during fixation in the binocular viewing condition. When the viewing condition changed from binocular to monocular, three patients (25%) had atypical shifts in their eye position. The shift was related to (1) loss of fixational control when the better eye was covered and the worse eye viewed the target or (2) a slow drift of the viewing eye that was associated with a large phoria in the covered eye. CONCLUSIONS: Patients with AMD have good binocular ocular motor coordination during fixation. A change in viewing condition from binocular to monocular can lead to disturbances in ocular motor control for some patients, especially in the worse eye.
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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 | 0.001 | 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".