Spatial and Global Sensory Suppression Mapping Encompassing the Central 10° Field in Anisometropic Amblyopia
Bibliographic record
Abstract
Purpose: We investigate the efficacy of a novel dichoptic mapping paradigm in evaluating visual function of anisometropic amblyopes. Methods: Using standard clinical measures of visual function (visual acuity, stereo acuity, Bagolini lenses, and neutral density filters) and a novel quantitative mapping technique, 26 patients with anisometropic amblyopia (mean age = 19.15 ± 4.42 years) were assessed. Two additional psychophysical interocular suppression measurements were tested with dichoptic global motion coherence and binocular phase combination tasks. Luminance reduction was achieved by placing neutral density filters in front of the normal eye. Results: Our study revealed that suppression changes across the central 10° visual field by mean luminance modulation in amblyopes as well as normal controls. Using simulation and an elimination of interocular suppression, we identified a novel method to effectively reflect the distribution of suppression in anisometropic amblyopia. Additionally, the new quantitative mapping technique was in good agreement with conventional clinical measures, such as interocular acuity difference (P < 0.001) and stereo acuity (P = 0.005). There was a good consistency between the results of interocular suppression with dichoptic mapping paradigm and the results of the other two psychophysical methods (suppression mapping versus binocular phase combination, P < 0.001; suppression mapping versus global motion coherence, P = 0.005). Conclusions: The dichoptic suppression mapping technique is an effective method to represent impaired visual function in patients with anisometropic amblyopia. It offers a potential in "micro-"antisuppression mapping tests and therapies for amblyopia.
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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 | 0.000 |
| 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".