Barriers to successful dichoptic treatment for amblyopia in young children
Bibliographic record
Abstract
PURPOSE: In an ongoing randomised clinical trial comparing dichoptic VR video games with patching for amblyopia, we evaluated any potential barriers to successful use of this novel amblyopia treatment method. METHODS: From December 2017, all newly diagnosed amblyopic children were recruited. Excluded were children under age 4 and patients with strabismus exceeding 30PD. The video game was played for 1 h per week at the outpatient clinic under direct supervision. Records were kept of difficulties encountered during treatment and categorised into domains. Factors influencing the successful completion of this treatment were identified and related to patient characteristics. RESULTS: Ninety-one children were recruited for the trial, 20 parents refused participation before randomisation, because of the logistical challenges the outpatient dichoptic treatment would cause them. Of the 17 children who commenced dichoptic treatment (median age 6.2 years; IQR 4.9-8.4 years), 10 did not complete treatment. Children under age 5.5 years were unable to comprehend the game settings or the game itself. Older children (N = 7; 41%) were less willing to comply with the video game. Loss of interest in the game (N = 8; 47%) was found to be a limiting factor at all ages. CONCLUSION: Half of the children failed to complete VR dichoptic treatment, mainly due to young age. In countries with nationwide screening where amblyopia is detected before age 6, the applicability of such dichoptic treatment is limited.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".