Predictors of emergency service use in adolescents and adults with autism spectrum disorder living with family
Bibliographic record
Abstract
INTRODUCTION: The use of emergency services among adolescents and adults with autism spectrum disorder (ASD) transitioning into adult health services has not been well described. OBJECTIVES: To describe emergency service use including emergency departments (EDs), paramedics, and police involvement among adolescents and adults with ASD and to examine predictors of using emergency services. METHODS: Caregivers of 396 adolescents and adults with ASD were recruited through autism advocacy agencies and support programmes in Ontario to complete a survey about their child's health service use. Surveys were completed online, by mail and over the phone between December 2010 and October 2012. Parents were asked to describe their child's emergency service use and provide information about potential predictive factors including predisposing, enabling and clinical need variables. RESULTS: According to parents, 13% of their children with ASD used at least one emergency service in a 2-month period. Sedation or restraints were used 23% of the time. A combination of need and enabling variables predicted emergency service use with previous ED use in the last year (OR 3.4, 95% CI 1.7 to 6.8), a history of hurting others (OR 2.3, 95% 1.2 CI to 4.7) and having no structured daytime activities (OR 3.2, 95% CI 1.4 to 7.0) being the strongest multivariate predictors in the model. CONCLUSIONS: Patients with ASD and their families are likely to engage with paramedics or police or visit the ED. Further education and support to families and emergency clinicians are needed to improve and, when possible, prevent such occurrences.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".