Obstetrical Outcomes of Patients with Epilepsy in a Canadian Tertiary Care Center (2014–2020)
Bibliographic record
Abstract
BACKGROUND: There is a paucity of data on the obstetrical outcomes of Canadian pregnant patients with epilepsy, which may differ from the average Canadian pregnancy and from other populations of pregnant patients with epilepsy. METHODS: Pregnant patients with epilepsy were identified from a prospectively collected database of patients seen at the maternal-fetal medicine obstetrics program of Mount Sinai Hospital (Toronto, Canada) between January 1, 2014, and November 20, 2020. Pregnancy, delivery, and neonatal outcome data were retrieved from this database and described using 95% binomial confidence intervals. Comparisons of obstetrical outcomes over the same period among the Canadian population average, obtained from publicly available national health data, were done using one-proportion Z-tests for nominal variables and one-sample t-tests for continuous variables. RESULTS: = 0.44). CONCLUSION: In this cohort of Canadian pregnant patients with epilepsy from an urban tertiary care center, observed rates of obstetrical complications were rare and no higher than in the Canadian population over the same period, with the exception of cesarean section and postpartum hemorrhage. Future prospective studies that include primary care and rural settings are needed to increase the generalizability of those results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".