Learning to see after early and extended blindness: A scoping review
Bibliographic record
Abstract
Purpose: If an individual has been blind since birth due to a treatable eye condition, ocular treatment is urgent. Even a brief period of visual deprivation can alter the development of the visual system. The goal of our structured scoping review was to understand how we might better support children with delayed access to ocular treatment for blinding conditions. Method: We searched MEDLINE, Embase and Global Health for peer-reviewed publications that described the impact of early (within the first year) and extended (lasting at least 2 years) bilateral visual deprivation. Results: Of 551 reports independently screened by two authors, 42 studies met our inclusion criteria. Synthesizing extracted data revealed several trends. The data suggests persistent deficits in visual acuity, contrast sensitivity, global motion, and visual-motor integration, and suspected concerns for understanding complex objects and faces. There is evidence for resilience in color perception, understanding of simple shapes, discriminating between a face and non-face, and the perception of biological motion. There is currently insufficient data about specific (re)habilitation strategies to update low vision services, but there are several insights to guide future research in this domain. Conclusion: This summary will help guide the research and services provision to help children learn to see after early and extended blindness.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".