Training at-home on a dichoptic reading application improves visual function in adults with amblyopia
Bibliographic record
Abstract
Recent research on treatments for amblyopia has shifted its focus from conventional patching, which is only applicable in childhood, to exploring dichoptic tasks. This study leveraged a new approach using an important daily task, reading, to improve amblyopic vision. Here, we assessed if training at-home on a dichoptic E-book application can be an alternative treatment for binocular vision in amblyopia. The dichoptic reading application (DEBRA) was uploaded onto tablets displaying E-books in red/green/black presentation, with each word of text being one of the three colors. By using anaglyph red/green glasses different text could be shown to each eye simultaneously, forcing the individual to combine the input from both eyes. At an initial visit, adult amblyopic participants were given an ophthalmic assessment, then reading speed and eye movements patterns while reading were recorded. Next, participants brought the technology home and trained for one hour per day across two weeks. At the outcome visit they were reassessed on the tests from the initial visit and completed a visual comfort questionnaire. Preliminary results demonstrated improved visual acuity, and contrast sensitivity in most participants. There was variability in task compliance, with some participants being able to easily read everyday for an hour per day and others having more difficulty following this training protocol. Based on the visual comfort questionnaire responses, majority of participants did not experience visual discomfort while completing the dichoptic training for two weeks. Overall, this preliminary study demonstrated that daily training on a dichoptic reading application at home for two weeks can improve amblyopic visual function. More data will clarify if eye movement patterns and other altered ophthalmic factors in amblyopia can be treated by completing the training. Future steps are aimed at collecting more data from amblyopes and ameliorating the user-friendliness of the application.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".