The Prevalence of Autistic Spectrum Disorder in Children Surveyed in a Tertiary Care Epilepsy Clinic
Bibliographic record
Abstract
UNLABELLED: It is well documented that children with autistic spectrum disorder (ASD) have an increased prevalence of seizures; however, studies have not been done to evaluate the prevalence of ASD in children with epilepsy. This comorbidity is important to define as early diagnosis and intervention in some children with ASD has been shown to improve outcome. METHOD: Children with epilepsy seen in a tertiary care epilepsy clinic were evaluated using validated autism screening questionnaires (ASQ). In addition, questions about sleep-related disorders, behavior, seizure characteristics, antiepileptic agents, and body mass index (BMI) were requested. An attempt was then made to determine if there was a correlation between the factors identified and ASD. RESULTS: Of the 107 questionnaires returned, 97 ASQ's were properly completed and used in this study. Approximately 32% of children fit the ASQ criteria for having ASD. Most children had not been previously diagnosed. Worst behavior and daytime sleepiness was seen in those at greater risk (p < 0.01). Seizures also occurred earlier (approximately 2 years) in children at risk of having ASD. CONCLUSION: Though confirmatory diagnostic evaluations are needed, this questionnaire-based study suggests that children with epilepsy are at greater risk of having ASD, and illustrates the need for more clinical vigilance. Behavioral difficulties and daytime sleepiness identified in these children could potentially affect their ability to learn. It is of interest that the age of seizure onset identified in those at greater risk corresponds with the approximate age of regression identified in some children with ASD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".