Transformative Gamified Binocular Therapy for Unilateral Amblyopia in Young Children: Pilot Prospective Efficacy and Safety Study
Bibliographic record
Abstract
Background: Amblyopia is a common cause of visual impairment in children. Compliance with traditional treatments for amblyopia is challenging due to negative psychosocial impacts. Recent shifts in amblyopia treatment have moved from suppressing the dominant eye to enhancing binocular visual function. Binocular digital therapy has become a promising approach. Objective: The aim of this study was to evaluate the effects of binocular gamified digital therapy on visual acuity and stereoacuity (SA) in children with unilateral amblyopia. Methods: This pilot prospective study enrolled 11 children aged 4-6 years with unilateral amblyopia. Following at least 8 weeks of refractive correction, participants underwent binocular gamified digital therapy for 60 minutes per day, 5 days a week. The therapy used a roguelike shooting game delivered under binocular conditions through two independent channels with a real-time artificial intelligence visual engine. Assessments of distance visual acuity (DVA), near visual acuity (NVA), and SA were conducted at baseline and again at 4, 8, and 12 weeks. Results: At 12 weeks, the following significant improvements were noted: amblyopic eye DVA improved by 1.0 line (P=.01; d=0.77), binocular DVA improved by 0.7 lines (P=.006; d=1.00), and SA improved by 0.3 logarithm (log) arcseconds (P=.01; d=0.97). At 8 weeks, improvements included amblyopic eye DVA by 0.9 lines (P=.046; d=1.00) and SA by 0.28 log arcseconds (P=.02; d=0.90). No significant adverse events were observed, although one participant developed progressive esotropia. Conclusions: Binocular gamified digital therapy is effective and safe for improving visual outcomes in children aged 4-6 years with unilateral 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".