Epilepsy and Pregnancy: An Audit of Specialized Care
Bibliographic record
Abstract
BACKGROUND: Caring for women with epilepsy (WWE) during pregnancy poses unique challenges. We conducted an audit of the care our epilepsy clinic provided to pregnant WWE. METHODS: We performed a retrospective study on all pregnancies followed by an epileptologist at a Canadian tertiary care centre's epilepsy clinic between January 2003 and March 2021. Among 81 pregnancies in 53 patients, 72 pregnancies in 50 patients were analyzed to determine patient-related, follow-up-related, antiseizure-medication-related, and child-related pregnancy characteristics. Univariate analyses were performed to explore if these characteristics were associated with disabling seizure occurrence during pregnancy. RESULTS: Most pregnancies were intended (72%) and occurred in women who used folic acid pre-pregnancy (76%) and who followed recommended blood tests for antiseizure medication (ASM) levels (71%). In 49% of pregnancies, ASM dosage was modified; 53% of these modifications were made in response to ASM blood levels. Most often used ASMs were lamotrigine (43%), followed by carbamazepine (32%) and levetiracetam (13%). One child was born with a thyroglossal duct cyst; our congenital malformation rate was thus 2%. Disabling seizures occurred in 24% of pregnancies. Exploratory analyses suggested that disabling seizure occurrence during pregnancy was associated with younger patient age (p = 0.018), higher number of ASMs used during pregnancy (p = 0.048), lamotrigine usage in polytherapy (p = 0.008), and disabling seizure occurrence pre-pregnancy (p = 0.027). CONCLUSION: This Canadian audit provides an in-depth description of pregnancies benefiting from specialized epilepsy care. Our results suggest an association between disabling seizure occurrence during pregnancy and lamotrigine usage in polytherapy that warrants further evaluation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".