Expert consensus regarding indicators of a traumatic reaction in autistic youth: a Delphi survey
Bibliographic record
Abstract
OBJECTIVE: It has been suggested that the sequelae of trauma are under-recognized in youth on the autism spectrum. We aimed to generate expert consensus regarding important trauma indicators, including but not limited to traumatic stress symptoms, in autistic youth. METHODS: We recruited 72 experts in autism and/or childhood trauma. Via a 2-round Delphi survey, experts commented on and rated the importance of 48 potential indicators, drawn from PTSD criteria and a broader literature on traumatic sequelae in autism. A revised list of 51 indicators, 18 clinical guidelines developed from expert comments, and summaries of expert qualifications and ratings from Round 1 were submitted to a second round (n = 66; 92% retention) of expert review and rating. RESULTS: Twenty-two indicators reached consensus (>75% round 2 endorsement). Many, but not all, reflected PTSD criteria, including intrusions (e.g., trauma re-enactments in perseverative play/speech), avoidance of trauma-reminders, and negative alterations in mood/cognition (e.g., diminished interest in activities) and in arousal/reactivity (e.g., exaggerated startle). Experts also identified increased reliance on others, adaptive and language regressions, self-injurious behavior, and non-suicidal self-injury as important indicators. Consensus guidelines emphasized the need for tailored measures, developmentally informed criteria, and multiple informants to increase diagnostic accuracy. CONCLUSIONS: Expert consensus emphasizes and informs a need for tailored diagnostic guidelines and measures to more sensitively assess traumatic reactions in autistic youth.
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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 | 0.115 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".