Multi-Feature Mapping of Distortions in Amblyopia With Localized Sampling
Bibliographic record
Abstract
Purpose: The purpose of this study was to investigate position, orientation, and spatial frequency (SF) distortions in amblyopia, their distribution across the visual field (VF), and their relationship with visual acuity (VA) loss. Methods: Twenty-one participants with amblyopia were tested using three tasks measuring distortions in position, orientation, and SF. Stimuli were presented on a 6 × 6 grid covering the central 5 degrees of the VF, with participants adjusting the fellow eye's perception to match the amblyopic eye. Distortion maps were created for each type, and correlations were analyzed within subjects (across their 3 distortion maps) and between subjects (comparing the same type of distortion maps across participants). Correlations with VA loss were also assessed. Results: The prevalence of distortion maps varied, with SF distortions being the most dominant (88.9%), followed by position distortions (66.7%), and orientation distortions being the least common (22.2%). Distortions extended beyond the fovea. Within subjects, spatial patterns of distortion showed no significant correlations across distortion types (P > 0.05), indicating their independence. Between subjects, no significant correlations were found for the same type of distortion map, suggesting individual variability. Additionally, VA differences were not significantly correlated with any distortion type, reinforcing the independence of VA from perceptual distortions. Conclusions: This study highlights the importance of assessing multiple distortion types to fully characterize perceptual deficits in amblyopia. The findings suggest that no single distortion type fully represents amblyopic spatial distortion, as each operates independently. Distortion mapping is essential for understanding, monitoring improvements, and accurately diagnosing amblyopia, as VA measurements alone fail to address these deficits comprehensively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".