Assessing the interocular delay in amblyopia and its link to visual acuity
Bibliographic record
Abstract
Research on interocular synchronicity in amblyopia has demonstrated a deficit in synchronization (i.e. a neural processing delay) between the two eyes. Current methods for assessing interocular delay are either costly or only effective for assessments in mild amblyopia. In this study, we adapted a novel protocol developed by Burge & Cormack (2020) based on continuous psychophysics to measure the interocular delay on a wide range of amblyopes. The purpose of the current study is to assess the efficacy and accessibility of this protocol and determine whether the measurements of interocular synchronicity it produces are correlated with visual acuity. This protocol is performed in both binocular and monocular viewing conditions and consists of following a target undergoing lateral Brownian motion as closely as possible with the mouse curser. The lag between the target and cursor is computed for each eye by determining the offset between the stimulus and mouse sequence at which the cross-correlation coefficient is maximal. This lag reflects the processing delay for a given eye. Additionally, assessment of the quality of the correlation indicates the accuracy at which the target was tracked. Our results show that all but the most severe amblyopes successfully performed this task and exhibited interocular delay ranging from 1.6ms to 125.3ms. For the majority of amblyopes, this delay was attributed to the amblyopic eye. The correlations used to determine delays were generally high in quality but were lower in quality for the amblyopic eye. The magnitude of the delay was positively correlated with larger differences in interocular visual acuity. These results demonstrate the efficacy of this new protocol and further support the link between interocular synchronicity and amblyopia. These results could lead to the development of a universal interocular assessment procedure for diagnostic purposes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".