Identifying Children and Youth With Autism Spectrum Disorder in Electronic Medical Records: Examining Health System Utilization and Comorbidities
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder requiring significant health and educational resources for affected individuals. A reference standard for ASD was generated from an existing population-based cohort of 10,000 children and youth aged 1-24 years who were randomly selected for chart abstraction from 29,256 patients from 119 family physicians. We developed and validated an algorithm to identify children and youth with ASD within an electronic medical record system (N = 80,237, aged 1-24 years) in order to examine the prevalence of comorbidities and quantify health system utilization within the cohort. We identified 1,062 children and youth with ASD representing a prevalence of 1.32%. Compared to individuals without ASD, those with ASD had a higher prevalence of asthma, were more likely to visit a specialist, undergo surgery, and be hospitalized for psychiatric reasons. Children and youth with ASD in Ontario have complex health system needs, illustrated through a significant burden of comorbidities and increased health system utilization. LAY SUMMARY: Our paper generates population-based estimates of health system use by children and youth with ASD, who have a higher burden of comorbidities than the general population. We developed a case-finding algorithm and applied it in electronic medical records to create a cohort of children and youth with ASD, thereby generating an important resource to further study the health care needs of individuals with ASD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".