Predictors of irritability in pediatric autistic populations: a scoping review
Bibliographic record
Abstract
Introduction: Autism is characterized by social communication differences and repetitive behaviors, affecting 1%-2% of children. Irritability is a disabling condition affecting 19%-80% of autistic children. While extensive research has focused on interventions to reduce irritability symptoms, the underlying correlates remain poorly understood. To address this, we conducted a scoping review of the literature examining factors contributing to irritability in pediatric autistic populations. Methods: A literature search of Embase, MEDLINE, PubMed, PsycINFO, and Web of Science was conducted in addition to manually retrieved papers from Google Scholar. Studies underwent title and abstract screening by one reviewer and full-text screening by two reviewers; disagreements were resolved through deliberation. The remaining studies underwent data extraction. The review was conducted using the PRISMA-ScR checklist. Results: The search yielded 48 studies meeting the inclusion criteria. Correlates of irritability were categorized into eight themes: demographics/environmental, autism features, mental health, language, cognition, and function, neurobiological, physical health, physiological, and multidimensional factors. Findings revealed mixed associations with demographic factors, consistent positive associations with sensory differences and mental health symptoms, and varying associations with cognitive abilities and physical health factors. Neurobiological and physiological correlates were less explored. Conclusion: This review revealed a significant gap in understanding sociodemographic, phenotypic, and neurobiological and physiological correlates of irritability in autism. There was also a significant gap in understanding the multi-dimensional irritability correlates. Positive associations between irritability and sensory differences and mental health symptoms suggest potential avenues for investigation of non-medication interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".