Genetic analysis of very obese children with autism spectrum disorder
Bibliographic record
Abstract
Abstract Autism spectrum disorder (ASD) is defined by the triad of deficits in social interactions, deficits in communication, and repetitive behaviors. Common co-morbidities in syndromic forms of ASD include intellectual disability, seizures, and obesity. We asked whether very obese children with ASD had different behavioral, physical and genetic characteristics compared to children with ASD who were not obese. We found that very obese children with ASD had significantly poorer scores on standardized behavioral tests. Very obese boys with ASD had lower full scale IQ and increased impairments with respect to stereotypies, communication and social skills. Very obese girls with ASD had increased impairments with respect to irritability and oppositional defiant behavior. We identified genetic lesions in a subset of the children with ASD and obesity and attempted to identify enriched biological pathways. Our study demonstrates the value of identifying co-morbidities in children with ASD as we move forward towards understanding the biological processes that contribute to this complex disorder and prepare to design customized treatments that target the diverse genetic lesions present in individuals with ASD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".