Characteristics of fetal anticonvulsant syndrome associated autistic disorder
Bibliographic record
Abstract
The aim of this study was to evaluate the clinical features and frequency of autistic disorder or Asperger syndrome (AS; according to Diagnostic and Statistical Manual of Mental Disorders, 4th edition [DSM-IV] criteria) in children exposed to anticonvulsant medication in utero. During a 20-year study period, 626 children were born in Aberdeen to mothers taking antiepileptic drugs (AEDs). The study examined long-term effects of prenatal exposure to AEDs in 260 children (122 males, 138 females). Of these, 26 (16 males) were reported by parents to have social or behavioural difficulties. Eleven children (6 males, 5 females) fulfilled the DSM-IV criteria for autistic disorder and one (female) fulfilled the DSM-IV criteria for AS. These children comprised 4.6% of the exposed children studied, and 1.9% of all exposed children born during the study period. Mean age of these children at diagnosis was 5 years 4 months (SD 2y 11mo) and 9 years 10 months (SD 3y 10mo) at the time of this study. Other children from the group of 26 had difficulties in areas of speech and language development and social communication but did not meet the criteria for an autism spectrum disorder (ASD). Sodium valproate was the drug most commonly associated with autistic disorder, five of 56 (8.9%) of the study children exposed to sodium valproate alone had either autistic disorder or AS. It was concluded that prenatal exposure to anticonvulsant medication is a risk factor for the development of an ASD. Fetal anticonvulsant syndrome associated autistic disorder is characterized by an even sex ratio, absence of regression or skill loss, and language delay in the absence of global delay.
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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 | 0.000 | 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) | 0.002 | 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".