Letter Distortion Mapping in Amblyopia: Spatial Patterns, Stability, and Relationship to Visual Acuity
Bibliographic record
Abstract
Purpose: To investigate whether letter-based perceptual distortions in amblyopia follow spatially consistent patterns across different letters and to determine if these spatial distortion maps are letter specific or reflect a common underlying spatial organization of visual distortion in the amblyopic eye. Methods: Twenty-one individuals with amblyopia completed a distortion mapping task using the letters A, D, and E, shown at 36 visual field locations. Each letter was first viewed with the fellow eye and then with the amblyopic eye. Participants reported distortions, which were recorded to generate binary spatial maps. The task was repeated over three sessions to assess within-subject consistency, and spatial correlations were analyzed across letters and subjects. Results: Letter distortions were reported by 95% of participants and remained consistent across sessions. Within subjects, spatial distortion maps were significantly correlated across letters in 62% of cases (P ≤ 0.028), suggesting shared spatial patterns. However, across subjects, maps were largely uncorrelated, indicating individualized distortion profiles. No single letter consistently showed more distortion across the group, χ2(2) = 1.279, P = 0.5. A strong positive correlation was found between interocular visual acuity difference and overall distortion intensity (r = 0.70, P < 0.001), consistent across all letters. Conclusions: Letter distortions in amblyopia are highly prevalent, reliable, and spatially organized within individuals but idiosyncratic across subjects. These distortions correlate strongly with visual acuity loss, highlighting their potential as a clinically valuable and perceptually relevant measure for characterizing amblyopic visual dysfunction.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".