Assessing global developmental delay across instruments in minimally verbal preschool autistic children: The importance of a multi‐method and multi‐informant approach
Bibliographic record
Abstract
Intellectual assessment in preschool autistic children bears many challenges, particularly for those who have lower language and/or cognitive abilities. These challenges often result in underestimation of their potential or exclusion from research studies. Understanding how different instruments and definitions used to identify autistic preschool children with global developmental delay (GDD) affect sample composition is critical to advance research on this understudied clinical population. This study set out to examine the extent to which using different instruments to define GDD affects sample composition and whether different definitions affect resultant cognitive and adaptive profiles. Data from the Mullen Scales of Early Learning and the Vineland Adaptive Behavior Scales-Second Edition, a parent-report tool, were analyzed in a sample of 64 autistic and 73 neurotypical children (28-69 months). Our results highlight that cognitive assessment alone should not be used in clinical or research practices to infer a comorbid diagnosis of GDD, as it might lead to underestimating autistic children's potential. Instead, using both adaptive and cognitive levels as a stratification method to create subgroups of children with and without GDD might be a promising approach to adequately differentiate them, with less risk of underestimating them.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".