Weight status of children and adolescents with autism spectrum disorder: A cross‐sectional analysis of primary care electronic medical records and linked health administrative datasets in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Individuals with autism spectrum disorder (ASD) may be at increased risk of both obesity and underweight. OBJECTIVE: To examine the association between ASD and weight status in children and adolescents, adjusting for individual- and neighbourhood-level sociodemographic factors. METHODS: We conducted a cross-sectional study of children and adolescents ≥2 and ≤18 years old using health administrative and demographic data from Ontario, Canada. Using growth measurements from a large primary care database between 2011 and 2016, we categorized weight status using World Health Organization definitions. We defined ASD based on a previously validated algorithm. RESULTS: We included 568 children and adolescents with ASD and 32 967 without ASD. Comparing those with ASD to those without ASD, prevalence of underweight was 3.5% versus 1.9%, overweight 19.0% versus 18.2%, obesity 12.9% versus 7.3%, and severe obesity 5.8% versus 2.2%. In the fully adjusted multinomial logistic regression model, ASD remained associated with underweight (adjusted odds ratio [aOR] 2.02; 95% confidence interval [CI] 1.27-3.20), obesity (aOR 1.87; 95% CI 1.44-2.43) and severe obesity (aOR 2.62; 95% CI 1.81-3.80). CONCLUSION: Children and adolescents with ASD are at increased risk of underweight, obesity, and severe obesity, independent of sociodemographic characteristics. Strategies addressing growth and weight status are warranted in this population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".