P.020 The North American AED Pregnancy Registry: A Canadian Subgroup Analysis (1997-2019)
Bibliographic record
Abstract
Background: This study aims to provide data on the care of pregnant women with epilepsy (pWWE) that is directly applicable to the Canadian context. Methods: Between 1997 and 2019, pWWE from Canada and the USA who enrolled into the North American AED Pregnancy Registry (NAARP) completed a questionnaire on their AED (anti-epileptic drug) usage. Enrollment rates to NAARP were compared between the two countries, and between the different Canadian provinces using population-based enrollment rate ratios (PERR). The AED prescription pattern among Canadian pWWE was analysed and compared with the USA. Results: During the study period, 10,215 women enrolled into NAARP : 4.1% (n=419) were Canadian, below the expected population-based contribution (PERR=0.42; p<0.01). Within Canada, the three northern territories (PERR=0; p<0.01), Prince-Edward Island (PERR=0; p<0.01), and Quebec (PERR=0.41; p<0.01) had the lowest enrollment rate ratios. Lamotrigine was the most commonly prescribed AED among canadian pWWE; they were, however, more likely to be on polytherapy (25%; p=0.13), on Carbamazepine (24%; p<0.01) or valproic acid (21%; p<0.01) than their American counterparts. Conclusions: Greater enrollment of Canadian women to NAARP, through enhanced clinician referrals, in particular from underrepresented provinces/territories, could lead to more accurate population-specific data and help identify gaps in the care of this vulnerable patient population.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".