The Remarkably High Prevalence of Epilepsy and Seizure History in Fetal Alcohol Spectrum Disorders
Bibliographic record
Abstract
BACKGROUND: Fetal alcohol spectrum disorder (FASD) is the umbrella term that describes the range of adverse developmental outcomes that may occur in the offspring of mothers who drink alcohol during pregnancy. FASD is associated with several comorbidities including epilepsy. The objective of the study was to evaluate the prevalence of epilepsy or a history of seizures in subjects with FASD and the contribution of relevant risk factors. METHODS: A retrospective chart review was conducted on all active charts (N = 1063) at two FASD clinics. After exclusion of subjects without a confirmed diagnosis, a total of 425 subjects between the ages of 2-49 were included in the analysis. The relationships between FASD diagnosis and other risk factors for co-occurrence of epilepsy or a seizure disorder (e.g., extent of exposure to alcohol and other drugs, type of birth, and trauma) were examined using chi-square and multivariate multinomial logistic regression. RESULTS: Twenty-five (5.9%) individuals in the study population had a confirmed diagnosis of epilepsy, and 50 (11.8%) had at least one documented seizure episode, yielding an overall prevalence of 17.7% in this population. Importantly, a history of epilepsy or seizures was not different across the three diagnostic subgroups. In those subjects with available maternal drinking histories, first trimester exposure or drinking throughout all three trimesters were the predominant forms of fetal exposure. None of the other risk factors were associated with a greater prevalence of epilepsy or seizures. CONCLUSIONS: There is a remarkably high prevalence of epilepsy/seizures in the FASD population.
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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 | 0.004 |
| 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".