Exploring Another Piece of the Puzzle: Comorbid Mental Health Challenges in Children with Autism Spectrum Disorder
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is characterized by impairments in social communication and restricted, repetitive behavioural patterns.1 In the United States, the prevalence of ASD is estimated to be 1 in 68 children.2 As children with ASD enter adolescence, they face difficulties brought on not only by the core functional impairments of their primary disorder but also by common psychiatric comorbidities, such as depression and anxiety. The prevalence estimates of these comorbidities vary widely, which may be explained by differences in assessment tools used between epidemiological studies. In this review, the most commonly used assessment tools for comorbidities among youth with ASD are presented. Out of the five tools identified, no tools had been specifically evaluated to measure anxiety among ASD youth, while only two tools had been evaluated to measure depression in this population. Across tools, there were numerous issues regarding psychometric properties, as well as differences in informants, leading to limitations when using these tools to measure comorbid psychiatric concerns in youth with ASD. Future research should aim to design better assessment tools to strengthen the quality of research in this field and better inform resource allocation and clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".