Effect of dichoptic video game treatment on mild amblyopia – a pilot study
Bibliographic record
Abstract
PURPOSE: The effect of contrast-balanced dichoptic video game training on distance visual acuity (DVA) and stereo acuity has been investigated in severe-to-moderate amblyopia, but its effect on mild amblyopia and fixation stability has not been assessed. This pilot study aimed to evaluate the effect of home-based dichoptic video game on amblyopic eye DVA, stereo acuity and fixation stability in adults with mild amblyopia. METHODS: A randomized single-masked design was adopted. The active 6-week home-based treatment was an anaglyphic, contrast-balanced dichoptic video game, and the placebo was an identical non-dichoptic game. Participants (n = 23) had mild amblyopia (amblyopic DVA ≤ 0.28 log Minimum Angle of Resolution (logMAR)). The primary outcome was change in amblyopic DVA at 6 weeks postrandomization. Near visual acuity, stereo acuity and fixation stability (bivariate contour eclipse area) were also measured. Follow-up occurred at 12 and 24 weeks postrandomization. RESULTS: Mean amblyopic eye DVA was 0.21 ± 0.06 and 0.18 ± 0.06 logMAR for the active (n = 12) and placebo (n = 11) group, respectively. Amblyopic DVA improved significantly more in the active group (0.09 ± 0.05) than in the placebo group (0.03 ± 0.04 logMAR; p < 0.05). The difference between groups remained at 12 weeks postrandomization (p = 0.04) but not at 24 weeks (p = 0.43). Titmus stereo acuities improved significantly more in the active group (0.40 log arcsec) than in the placebo group (0.09 log arcsec) after 6 weeks of gameplay. The between-group difference was still present at 24 weeks postrandomization (p = 0.05). There were no differences between groups on any other secondary outcomes. CONCLUSION: Home-based dichoptic video gameplay may be an effective method to improve amblyopic DVA and stereo acuity in mild 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".