The Measurement and Treatment of Suppression in Amblyopia
Bibliographic record
Abstract
Amblyopia, a developmental disorder of the visual cortex, is one of the leading causes of visual dysfunction in the working age population. Current estimates put the prevalence of amblyopia at approximately 1-3%1-3, the majority of cases being monocular2. Amblyopia is most frequently caused by ocular misalignment (strabismus), blur induced by unequal refractive error (anisometropia), and in some cases by form deprivation. Although amblyopia is initially caused by abnormal visual input in infancy, once established, the visual deficit often remains when normal visual input has been restored using surgery and/or refractive correction. This is because amblyopia is the result of abnormal visual cortex development rather than a problem with the amblyopic eye itself4,5 . Amblyopia is characterized by both monocular and binocular deficits6,7 which include impaired visual acuity and poor or absent stereopsis respectively. The visual dysfunction in amblyopia is often associated with a strong suppression of the inputs from the amblyopic eye under binocular viewing conditions8. Recent work has indicated that suppression may play a central role in both the monocular and binocular deficits associated with amblyopia9,10 . Current clinical tests for suppression tend to verify the presence or absence of suppression rather than giving a quantitative measurement of the degree of suppression. Here we describe a technique for measuring amblyopic suppression with a compact, portable device11,12 . The device consists of a laptop computer connected to a pair of virtual reality goggles. The novelty of the technique lies in the way we present visual stimuli to measure suppression. Stimuli are shown to the amblyopic eye at high contrast while the contrast of the stimuli shown to the non-amblyopic eye are varied. Patients perform a simple signal/noise task that allows for a precise measurement of the strength of excitatory binocular interactions. The contrast offset at which neither eye has a performance advantage is a measure of the "balance point" and is a direct measure of suppression. This technique has been validated psychophysically both in control13,14 and patient6,9,11 populations. In addition to measuring suppression this technique also forms the basis of a novel form of treatment to decrease suppression over time and improve binocular and often monocular function in adult patients with amblyopia12,15,16 . This new treatment approach can be deployed either on the goggle system described above or on a specially modified iPod touch device15.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".