The Regional Extent of Suppression: Strabismics Versus Nonstrabismics
Bibliographic record
Abstract
PURPOSE: Evidence is accumulating that suppression may be the cause of amblyopia rather than a secondary consequence of mismatched retinal images. For example, treatment interventions that target suppression may lead to better binocular and monocular outcomes. Furthermore, it has recently been demonstrated that the measurement of suppression may have prognostic value for patching therapy. For these reasons, the measurement of suppression in the clinic needs to be improved beyond the methods that are currently available, which provide a binary outcome. METHODS: We describe a novel quantitative method for measuring the regional extent of suppression that is suitable for clinical use. The method involves a dichoptic perceptual matching procedure at multiple visual field locations. We compare a group of normal controls (mean age: 28 ± 5 years); a group with strabismic amblyopia (four with microesotropia, five with esotropia, and one with exotropia; mean age: 35 ± 10 years); and a group with nonstrabismic anisometropic amblyopia (mean age: 33 ± 12 years). RESULTS: The extent and magnitude of suppression was similar for observers with strabismic and nonstrabismic amblyopia. Suppression was strongest within the central field and extended throughout the 20° field that we measured. CONCLUSIONS: Suppression extends throughout the central visual field in both strabismic and anisometropic forms of amblyopia. The strongest suppression occurs within the region of the visual field corresponding to the fovea of the fixing 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".