Assessment of Autism Spectrum Disorders in Children with Visual Impairment and Blindness
Bibliographic record
Abstract
Children with visual impairment and blindness (VIB) are consistently reported to show higher rates of autism spectrum disorder (ASD) or ASD-like features than sighted peers, yet the nature of this association remains unclear. A major source of ambiguity lies in the use of assessment tools developed for sighted populations, as these tools rely heavily on visual behaviours such as gaze following, joint attention, and eye contact. In children with VIB, these markers may reflect sensory differences rather than underlying neurodevelopmental disorders, increasing the risk of misdiagnosis. This commentary critically reviews recent adaptations of standard instruments, alongside the emergence of specialized measures. While adaptations and innovations show promise, their limited validation and integration into clinical practice hinder their impact. The present commentary builds on the findings of the scoping review by Stevenson & Tedone, 2024, which examined studies published between 1995 and 2020. The present work reflects on that body of evidence and notes that additional work since continues to shape understanding in this area. Taken together, these issues highlight the need for assessment frameworks that move beyond sighted developmental norms, prioritizing tools and training designed for non-sighted children. Only with rigorously validated instruments, longitudinal research and formalized guidelines can clinicians distinguish between neurodevelopmental disorders and expected development in children with VIB.
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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 | 0.013 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".