Seizure prediction in pregnant women with epilepsy: An umbrella review of clinical practice guidelines and systematic reviews
Bibliographic record
Abstract
OBJECTIVE: To identify risk factors for seizure in pregnant women, and in the general population with epilepsy. STUDY DESIGN: Umbrella review of clinical practice guidelines and systematic reviews on risk factors or prediction models for seizure occurrence in pregnant women with epilepsy, adults with epilepsy, or all individuals with epilepsy. Guidelines or systematic reviews exclusively for children were excluded. We searched MEDLINE, Emcare, Embase, CINAHL, TRIP PRO, Epistemonikos, World Health Organisation, Guideline International Network, DANS, and grey literature (2000-2023) without language restrictions. Risk factors or predictors listed in the final guidelines or systematic reviews were collated and thematically analysed. RESULTS: From 3406 citations, we included 13 articles (ten guidelines, three systematic reviews) reporting 26 risk factors in pregnant women and the general adult population with epilepsy: eight factors in guidelines for pregnant women only; five in both pregnant women and general adult populations (four in both guidelines and systematic reviews, one in guidelines only); and 13 factors in the general adult population (four in both guidelines and systematic reviews, eight in guidelines, and one in a systematic review). Risk factors were categorised into five broad themes: seizure type; seizure control; anti-seizure medication; neurological; and epilepsy and medical history. Three risk factors for seizure ocurrence were cited in more than two guidelines or systematic reviews: seizure freedom (reduced risk), immediate initiation of anti-seizure medication after first seizure (reduced risk), and abnormal electroencephalogram (increased risk). Three risk factors were linked to a more than two-fold chance of seizures in pregnant women with epilepsy: tonic-clonic seizures in the last three months (RR 7.20, 95% CI 6.63-11.93), a history of non-tonic-clonic seizures (RR 2.11, 95% CI 1.88-2.62), and seizures in the pre-pregnancy year compared to no seizures (RR 3.51, 95% CI 3.13-3.94). CONCLUSION: Multiple risk factors have been recommended for use in practice across different guidelines and reviews to identify those at increased risk of seizures in the adult population with epilepsy, and specifically in pregnant women with epilepsy. Further research is needed on the implementation of tools for predicting seizures to improve maternal and neonatal outcomes.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".