The role of suppression in amblyopia
Bibliographic record
Abstract
Abstract Purpose This study had three main aims; to assess the degree of suppression in patients with strabismic, anisometropic and mixed amblyopia, to establish the relationship between suppression and the degree of amblyopia and to compare the degree of suppression across the clinical sub‐groups within our sample. Methods Using both standard measures of suppression (Bagolini lenses and ND filters, Worth 4 dots) and a new approach involving the measurement of dichoptic motion thresholds under conditions of variable interocular contrast, we quantified the degree of suppression in 43 amblyopic patients with strabismus, anisometropia or a combination of both. Results There was good agreement between the quantitative measures of suppression made using the new dichoptic motion threshold technique and measurements made using standard clinical techniques (Bagolini lenses and ND filters, Worth 4 dots). The degree of suppression was found to directly correlate with the degree of amblyopia within our clinical sample whereby stronger suppression was associated with a greater interocular acuity difference and poorer stereoacuity. Suppression was not related to the type or angle of strabismus when this was present or the previous treatment history. Conclusion These results suggest that suppression may have a primary role in the amblyopia syndrome and therefore have implications for the treatment of 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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".