166: Prevalence of Overweight and Obese Children and Adolescents with Autism Spectrum Disorder from Alberta, Canada
Bibliographic record
Abstract
In Canada, it is estimated that nearly one in three children are overweight or obese (Roberts et al., 2012). With a myriad of contributing factors and potential for serious long-term health consequences, overweight/obesity status constitutes an urgent and complex public health issue (Avis et al., 2014). Moreover, recent reports suggest that approximately 35–38% in American children with Autism Spectrum Disorder (ASD) are overweight or obese, higher than the general population (Sharp et al., 2014; Zuckerman et al., 2014). To date, no studies have looked at weight status among children with ASD in a Canadian population. To determine the prevalence of overweight or obesity in a clinical sample of children with ASD at a tertiary care centre. Participants were recruited within the first 12 months of receiving their diagnosis of ASD (as defined by the DSMIV-TR) as part of an international registry. The primary health care provider (e.g. nurse practitioner, developmental pediatrician, etc.) completed several baseline measures, including age, gender, height, and weight. BMI was calculated for each participant using date of birth, date assessed, gender, height, and weight using the recommended BMI calculations by the Centre for Disease Control (2000) and terminology provided by Barlow et al. (2007). Complete information was available for 130 children with ASD (108 boys). Median age of participants was 5 years, 9 months (range: two years, two months – 17 years, four months; standard deviation: 3 years, 5 months). In total, 2% of children had BMI under the 5th percentile (underweight), 55% of children had BMI within the 5th to 85th percentile (normal weight), 41% of children were at or above the 85th percentile (overweight or obese) and 21% of children were at or above the 95th percentile (obese). Using a one-sample binomial test, children and adolescents with ASD were found to be significantly different in the prevalence of overweight and obese rates compared to typically developing Canadian children and adolescents (p.05). These findings are in accordance with the prevalence of overweight and obese status in children with ASD in the USA but differ from typically developing Canadian children. Further monitoring is needed longitudinally to monitor the prevalence of overweight and obese children and adolescents with ASD and to provide appropriate weight-management intervention.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".