Harnessing brain plasticity to improve binocular vision in amblyopia: An evidence-based update
Bibliographic record
Abstract
Amblyopia is a developmental visual disorder resulting from atypical binocular experience in early childhood that leads to abnormal visual cortex development and vision impairment. Recovery from amblyopia requires significant visual cortex neuroplasticity, i.e. the ability of the central nervous system and its synaptic connections to adapt their structure and function. There is a high level of neuroplasticity in early development and, historically, neuroplastic responses to changes in visual experience were thought to be restricted to a "critical period" in early life. However, as our review now shows, the evidence is growing that plasticity of the adult visual system can also be harnessed to improve vision in amblyopia. Amblyopia treatment involves correcting refractive error to ensure clear and equal retinal image formation in both eyes, then, if necessary, promoting the use of the amblyopic eye by hindering or reducing visual input from the better eye through patching or pharmacologic therapy. Early treatment in children can lead to visual acuity gains and the development of binocular vision in some cases; however, many children do not respond to treatment, and many adults with amblyopia have historically been untreated or undertreated. Here we review the current evidence on how dichoptic training can be used as a novel binocular therapeutic approach to facilitate visual processing of input from the amblyopic eye and can simultaneously engage both eyes in a training task that requires binocular integration. It is a novel and promising treatment for amblyopia in both children and adults.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".