A Game Platform for Treatment of Amblyopia
Bibliographic record
Abstract
We have developed a prototype device for take-home use that can be used in the treatment of amblyopia. The therapeutic scenario we envision involves patients first visiting a clinic, where their vision parameters are assessed and suitable parameters are determined for therapy. Patients then proceed with the actual therapeutic treatment on their own, using our device, which consists of an Apple iPod Touch running a specially modified game application. Our rationale for choosing to develop the prototype around a game stems from multiple requirements that such an application satisfies. First, system operation must be sufficiently straightforward that ease-of-use is not an obstacle. Second, the application itself should be compelling and motivate use more so than a traditional therapeutic task if it is to be used regularly outside of the clinic. This is particularly relevant for children, as compliance is a major issue for current treatments of childhood amblyopia. However, despite the traditional opinion that treatment of amblyopia is only effective in children, our initial results add to the growing body of evidence that improvements in visual function can be achieved in adults with 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".