Late Pregnancy Antiseizure Medication Exposure and Offspring Neurodevelopmental Risk: A Multi‐Child Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Antiseizure medication (ASM) use during pregnancy has increased over the past decade. However, evidence linking prenatal ASM exposure to neurodevelopmental disorders (NDDs) in offspring remains inconsistent. This study evaluated whether prenatal ASM exposure increases the risk of NDDs in children. METHODS: We analyzed data from 5 population-based cohorts of live-born children in Canada (Alberta, Manitoba, Ontario, Quebec; the Canadian Mother-Child Cohort [CAMCCO] cohorts) and the United States (AM-PREGNANT cohort). ASM exposure was defined as maternal prescription fills overlapping the 60 days before birth. NDDs were identified using validated algorithm based on the International Classification of Disease-9/10 codes from inpatient and outpatient records. Within each cohort, Cox proportional hazards models were applied, with adjustment performed separately using (1) covariates and (2) propensity scores. Pooled estimates were obtained using random-effects meta-analysis. RESULTS: Of 2,910,206 children, 0.47% were exposed to ASMs in the 60 days before birth. Prenatal ASM exposure was associated with a 29% increased risk of NDDs (pooled-adjusted hazard ratio [p-aHR], 1.29; 95% CI: 1.22-1.37; 1,805 exposed cases). In the Canadian cohorts, risks of combined NDDs varied by medication: carbamazepine (p-aHR: 1.50; 95% CI: 1.20-1.87; 262 exposed cases), clonazepam (p-aHR 1.22; 95% CI: 1.12-1.33; 585 exposed cases), topiramate (p-aHR 1.56; 95% CI: 1.04-2.34; 69 exposed cases), and valproic acid (p-aHR 1.38; 95% CI: 1.16-1.65; 134 exposed cases). Although point estimates were higher for polytherapy than monotherapy, the difference was not statistically significant. INTERPRETATION: Prenatal exposure to certain ASMs was consistently associated with increased risks of NDDs in offspring. These findings support careful, individualized decision-making regarding prenatal ASM use to minimize neurodevelopmental risks. ANN NEUROL 2026;99:761-776.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".