Identifying Absolute Preferred Retinal Locations during Binocular Viewing
Bibliographic record
Abstract
PURPOSE: We present a new method for identifying the absolute location (i.e., relative to the optic disc) of the preferred retinal location (PRL) simultaneously for the two eyes of patients with central vision loss. For this, we used a binocular eye-tracking system that determines the pupillary axes of both eyes without a user calibration routine. METHODS: During monocular viewing, we measured the pupillary axis and the angle between it and the visual axis (angle Kappa) for 10 eyes with normal vision. We also determined their fovea location relative to the middle of the optic disc with the MP-1 microperimeter. Then, we created a transformation between the eye-tracking and microperimeter measurements. We used this transformation to predict the absolute location of the monocular and binocular PRLs of nine patients with central vision loss. The accuracy of the monocular prediction was evaluated with the microperimeter. The binocular PRLs were checked for retinal correspondence and functionality by placing them on fundus photographs. RESULTS: The transformation yielded an average error for the monocular measures of 0.2 (95% confidence interval, 1.0 to -0.6 degrees) horizontally and 0.5 (95% confidence interval, 1.1 to -0.1 degrees) vertically. The predicted binocular measures showed that the PRLs were generally in corresponding locations in the two eyes. One patient whose PRLs were not in corresponding positions complained about diplopia. For all patients, at least one PRL fell onto functional retina during binocular viewing. CONCLUSIONS: This study shows that measurements of the location of the binocular PRLs relative to the pupillary axes can be transformed into absolute locations.
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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 | 0.002 |
| 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) | 0.001 | 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 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".