Irritability in autism examined through network analysis of phenotypic and physiological correlates
Bibliographic record
Abstract
Symptoms of irritability are commonly reported in autism, yet correlates of this domain remain poorly understood. While prevalence estimates in the literature vary considerably, they range up to 80%. Irritability can interfere with daily functioning, social relationships, and academic performance. Despite years of research and the availability of approved medications, intervention outcomes for irritability remain highly variable. To advance understanding and inform more targeted, personalized interventions, the present study aimed to clarify the correlates of irritability in autism using network analysis, an analytical tool suited to capture complex associations across interconnected variables. We examined demographic, phenotypic, and physiological factors in a sample of autistic and neurotypical children. Our findings identified strong direct associations between irritability and externalizing behaviors, emotion dysregulation, autism features, and negative affect. Physiological responses, including heart rate reactivity and variability, were indirectly connected to irritability through links with self-regulation abilities and ADHD traits. These results highlight the importance of conceptualizing irritability in autism as part of a broader, interconnected network of influences rather than an isolated symptom. Recognizing these relationships informs potential targets for future intervention studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".