Management issues for women with epilepsy—Focus on pregnancy (an evidence‐based review): II. Teratogenesis and perinatal outcomes
Bibliographic record
Abstract
A committee assembled by the American Academy of Neurology (AAN) reassessed the evidence related to the care of women with epilepsy (WWE) during pregnancy, including antiepileptic drug (AED) teratogenicity and adverse perinatal outcomes. It is highly probable that intrauterine first-trimester valproate (VPA) exposure has higher risk of major congenital malformations (MCMs) compared to carbamazepine (CBZ), and possibly compared to phenytoin (PHT) or lamotrigine (LTG). It is probable that VPA as part of polytherapy and possible that VPA as monotherapy contribute to the development of MCMs. AED polytherapy probably contributes to the development of MCMs and reduced cognitive outcomes compared to monotherapy. Intrauterine exposure to VPA monotherapy probably reduces cognitive outcomes and monotherapy exposure to PHT or phenobarbital (PB) possibly reduces cognitive outcomes. Neonates of WWE taking AEDs probably have an increased risk of being small for gestational age and possibly have an increased risk of a 1-minute Apgar score of <7. If possible, avoidance of VPA and AED polytherapy during the first trimester of pregnancy should be considered to decrease the risk of MCMs. If possible, avoidance of VPA and AED polytherapy throughout pregnancy should be considered and avoidance of PHT and PB throughout pregnancy may be considered to prevent reduced cognitive outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".