The North American Antiepileptic Drug Pregnancy Registry: A Canadian Subgroup Analysis
Bibliographic record
Abstract
ABSTRACT: Background: The North American AED Pregnancy Registry (NAAPR) provides crucial data for understanding the risks of antiepileptic drug (AED) exposure in pregnancy. This study aims to quantify the Canadian contribution to NAAPR and compare AED usage in pregnancy in Canada and the USA. Methods: Enrollment rate ratios (ERR) to NAAPR, adjusted for the populations of women of childbearing age, were calculated for the USA, Canada, and for the different Canadian provinces. Methods of enrollment to NAAPR and AED usage were compared between the two countries using chi-squared tests. Results: Between 1997 and 2019, 10,215 pregnant women enrolled into NAAPR: 4.1% were Canadian (n = 432, ERR = 0.39, CI 95% = 0.35–0.43). Within Canada, no patients were enrolled from the three northern territories or from Prince Edward Island. While fewer patients than expected enrolled from Quebec (ERR = 0.35, CI 95% = 0.19–0.58), Nova Scotia had the highest enrollment rate (ERR = 1.55; CI 95% = 0.66–3.11). Compared with their American peers, Canadians were less likely to have been enrolled by their healthcare provider and more likely to have been enrolled via social media ( p < 0.01). Canadian women were more likely to be taking carbamazepine (24% vs. 15%; p < 0.01) or valproic acid (8% vs. 4%; p < 0.01). Conclusion: The proportion of Canadian enrollees into NAAPR was less than expected based on the relative population size of Canadian women of reproductive age. Greater Canadian enrollment to NAAPR would contribute to ongoing worldwide efforts in assessing the risks of AEDs use in pregnant women and help quantify rates of AED usage, major congenital malformations, and access to subspecialized epilepsy care within Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".