Comparison of Perinatal Outcomes for Women With and Without Epilepsy
Bibliographic record
Abstract
Importance: Pregnant women who have epilepsy need adequate engagement, information, and pregnancy planning and management to improve pregnancy outcomes. Objective: To investigate perinatal outcomes in women with epilepsy compared with women without epilepsy. Data Sources: Ovid MEDLINE, Embase, CINAHL, and PsycINFO were searched with no language or date restrictions (database inception through December 6, 2022). Searches also included OpenGrey and Google Scholar and manual searching in journals and reference lists of included studies. Study Selection: All observational studies comparing women with and without epilepsy were included. Data Extraction and Synthesis: The PRISMA checklist was used for abstracting data and the Newcastle-Ottawa Scale for risk-of-bias assessment. Data extraction and risk-of-bias assessment were done independently by 2 authors with mediation conducted independently by a third author. Pooled unadjusted odds ratios (OR) or mean differences were reported with 95% CI from random-effects (I2 heterogeneity statistic >50%) or fixed-effects (I2 < 50%) meta-analyses. Main Outcomes and Measures: Maternal, fetal, and neonatal complications. Results: Of 8313 articles identified, 76 were included in the meta-analyses. Women with epilepsy had increased odds of miscarriage (12 articles, 25 478 pregnancies; OR, 1.62; 95% CI, 1.15-2.29), stillbirth (20 articles, 28 134 229 pregnancies; OR, 1.37; 95% CI, 1.29-1.47), preterm birth (37 articles, 29 268 866 pregnancies; OR, 1.41; 95% CI, 1.32-1.51) and maternal death (4 articles, 23 288 083 pregnancies; OR, 5.00; 95% CI, 1.38-18.04). Neonates born to women with epilepsy had increased odds of congenital conditions (29 articles, 24 238 334 pregnancies; OR, 1.88; 95% CI, 1.66-2.12), neonatal intensive care unit admission (8 articles, 1 204 428 pregnancies; OR, 1.99; 95% CI, 1.58-2.51), and neonatal or infant death (13 articles, 1 426 692 pregnancies; OR, 1.87; 95% CI, 1.56-2.24). The increased odds of poor outcomes was increased with greater use of antiseizure medication. Conclusions and Relevance: This systematic review and meta-analysis found that women with epilepsy have worse perinatal outcomes compared with women without epilepsy. Women with epilepsy should receive pregnancy counseling from an epilepsy specialist who can also optimize their antiseizure medication regimen before and during pregnancy.
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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 | 0.018 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".