Bridging the gap: unveiling key links between autism and anxiety symptoms in autistic children and youth using a network analysis in pooled data from four countries
Bibliographic record
Abstract
BACKGROUND: Autistic children experience significantly higher rates of anxiety compared to nonautistic children. The precise relations between autism characteristics and anxiety symptoms remain unclear in this population. Previous work has explored associations at the domain level, which involve examining broad categories or clusters of symptoms, rather than the relationships between specific symptoms and/or individual characteristics. We addressed this gap by taking a network approach to understand the shared structure of autism characteristics and anxiety symptoms. METHOD: Data were pooled from five studies from Canada, Singapore, the UK, and the USA, totaling 623 autistic children (17% female sex; aged 6-18 years), for whom the parent-report Spence Children's Anxiety Scale (SCAS-P) was available. We derived two undirected regularized networks, first from the SCAS-P items only, and then by adding autism characteristics pertaining to social communication, highly focused and repetitive behavior, and sensory hypersensitivity. From these models' metrics, we extracted nodes' predictability, key bridging nodes, and community detection. RESULTS: The anxiety-only network was highly connected and consisted of four key clusters: General Anxiety, Social Anxiety, Separation Anxiety, and Panic/Agoraphobia. These broadly aligned with the existing SCAS-P structure based on DSM-IV-TR criteria. In the autism-anxiety network, the structure of anxiety remained mostly stable, with autism features forming their own community. Preference for predictability (i.e., sameness) and sensory hypersensitivity were key nodes that linked autistic features and anxiety symptoms, primarily through generalized anxiety. CONCLUSION: This study identified some of the key characteristics that bridge the broadly independent structures of autism characteristics and anxiety symptoms. The findings are discussed in the context of guiding the assessment, prevention, and treatment of anxiety in autism.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".