Anxiety in children and adolescents with autism spectrum disorder: behavioural phenotypes and environmental factors
Bibliographic record
Abstract
BACKGROUND: Anxiety is the most prevalent comorbidity among children and adolescents with autism spectrum disorder (ASD), yet little is known about the associated risk factors. METHODS: In a heterogenous cohort of children aged 5-18 years old (n = 262, 42% ASD), participants and their parents completed standardized questionnaires to assess anxiety, ASD symptom severity, inattention/hyperactivity, emotional problems, depressive symptoms, parental styles and stress, and demographic factors. RESULTS: An artificial neural network analysis using a self-organizing map, a statistical technique used to cluster large datasets, revealed 3 distinct anxiety profiles: low (n = 114, 5% ASD), moderate (n = 70, 64% ASD) and high (n = 78, 96% ASD) anxiety. A recursive feature elimination analysis revealed that depression and peer problems contributed the most to differences between the anxiety profiles. Difficulties with peers in individuals with ASD who experience anxiety may be related to challenges with social competence and this may heighten depressive symptoms. CONCLUSION: Findings highlight the importance of assessing depressive symptoms in children and adolescents with ASD who experience anxiety. Identifying anxiety profiles among children and adolescents with ASD may prove beneficial in clinical practice by facilitating the development of tailored interventions that aid in managing anxiety and depressive symptoms. Furthermore, strengthening social communication skills may improve peer relationships and could aid in managing depressive symptoms among children and adolescents with ASD who experience anxiety.
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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 | 0.000 | 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".