Developing a novel dichoptic reading application for the treatment of amblyopia
Bibliographic record
Abstract
Current amblyopia treatment research has focused on binocular dichoptic tasks instead of the typical patching treatment which has low compliance rates and long-term effectiveness. This study aimed to use another entertaining and important daily task, reading, to improve binocular vision in amblyopia. Here, we assessed the feasibility of a dichoptic e-book application as an alternative treatment for binocular vision in amblyopia. A prototype of the application was developed and uploaded onto tablets that were used for participant assessments. Participants read e-books in anaglyph red/green/black presentation which allowed for monocular and binocular contrast to be adjusted independently. Amblyopic and control participants were then tested on their reading speed and questioned about their comfort using the application. We found that participants read slower in the dichoptic presentation than in the control presentation, indicating that their visual systems were forced to integrate information from both eyes. In some cases, reducing the contrast of text seen by the fellow eye also increased the reading speed of amblyopes in accordance with current research on binocular training approaches. Following the testing sessions that produced these results, participant feedback from the comfort questions was implemented into an improved application model. Overall, this study demonstrated that amblyopes can read binocularly within the e-book application framework suggesting that it could be an effective treatment for amblyopia. Future steps in this research are focused on training amblyopes on reading in this application to improve their binocular vision.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".