Identifying cognitive profiles in children with neurodevelopmental disorders using online cognitive testing
Bibliographic record
Abstract
Children with neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD) tend to exhibit similar deficits in attention and memory ability. Early screening of cognitive deficits in children with NDDs, particularly in preschool children, is fundamental to improving cognitive and academic outcomes. In order to determine cognitive profiles in children with ASD and ADHD, we developed accessible audiovisual instructions for an online battery of 13 cognitive tests. Children ages 4–16 who were diagnosed with ADHD ( n = 83), or ASD ( n = 37), or who were typically developing children (TD) ( n = 86) were recruited. Data were analyzed using a stepwise Discriminant Analysis to determine which cognitive tasks were the strongest discriminators between the diagnostic groups. Results revealed four tasks reflective of working memory, reasoning, and attentional processes, which correctly classified approximately 53–60% of each group. The ADHD group had lower scores on attentional tasks compared to TD, while ASD group had lower scores on reasoning tasks compared to the TD children, and made more attempts across all four tasks. The results from this study stress the need for cognitive screening assessments that include domain-specific items to improve the characterization of executive function deficits and promote academic achievement in all children with NDDs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".