Elevated Prevalence of Overweight & Obesity in Children with Autism Spectrum Disorders
Bibliographic record
Abstract
Participants included 5053 children enrolled in Autism Speaks’ Autism Treatment Network (ATN) from 2008 to 2013 at 19 sites in the US and Canada. The ATN registry includes children ages 2-17 with confirmed ASD per Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) criteria, supported by administration of the Autism Diagnostic Observation Schedule (ADOS). The ATN sample was compared to a general population sample from the National Health & Nutrition Examination Survey (NHANES). The NHANES survey is a representative cross-sectional sample of the US non-institutionalized population and is described elsewhere 2. Weight and height values in NHANES are collected via standardized physical examination. We utilized data from three consecutive NHANES surveys (6 years) to account for secular changes in prevalence of overweight or obesity. We restricted the sample to children aged 2-17 to match the age range in the ATN. Following the CDC analysis guidelines, prevalence and variance estimates were calculated after applying the mobile examination center (MEC) 6 year sample weights to take into account the complex sampling design using the Survey package in R, which utilizes Taylor Series Linearization methods for variance estimation. Confidence intervals were constructed using the logit transformation through the svyby option in Survey, as recommended by CDC analysis guidelines.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".