Antiseizure medication use during pregnancy and neonatal growth outcomes: A systematic review and meta‐analysis
Bibliographic record
Abstract
Aims We aimed to systematically synthesize the current published literature on neonatal growth outcomes associated with antiseizure medication (ASM) use during pregnancy. Methods We searched seven databases, from inception to 23 March 2022. We investigated small for gestational age (SGA) and low birth weight (LBW) as primary outcomes and birth weight, birth height, cephalization index and head circumference as secondary outcomes. The primary analysis included pregnant people exposed to any ASM compared with unexposed pregnant people. Subgroup analysis included ASM class analysis, within epilepsy group analysis and polytherapy compared to monotherapy. Results We screened 15 720 citations and included 65 studies in the review. Exposed pregnant people had a significantly increased risk of SGA relative risk (RR) 1.33 (95% CI 1.18 to 1.50, I 2 74%), LBW RR 1.54 (95% CI 1.33 to 1.77, I 2 67%), and decreased birth weight with a mean difference (MD) of −118.87 (95% CI −161.03 to −76.71, I 2 42%) g. A non‐significant risk change in birth height and head circumference was observed. In subgroup analysis, ASM polytherapy, within epilepsy and ASM class analysis were also associated with an increased risk of SGA and LBW. Conclusions This meta‐analysis demonstrates that pregnant people exposed to ASMs have a significantly increased risk of adverse fetal growth outcomes including SGA and LBW and decreased birth weight compared to unexposed pregnant people. Polytherapy was associated with higher risks compared to monotherapy. Additional studies are warranted on specific ASM risks.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".