Abstract WP183: Pregnancy-related Factors Associated With Stroke In Mothers And Newborns: Data From The International Maternal Newborn Stroke Registry
Bibliographic record
Abstract
Background: Maternal and newborn stroke occur during the same period when physiological and anatomic connections between mother and infant may provide a rationale for common pathogenetic mechanisms. The similarities and differences between maternal and newborn stroke remain poorly understood. We created the International Maternal Newborn Stroke Registry - the first database to explore these relationships. Methods: Eleven international sites participated; 9 enrolled cases for this analysis. Eligible cases were newborns (from 28 weeks gestation to 28 postnatal days) or mothers (peripartum or within 6 weeks postpartum) diagnosed with stroke (ischemic, hemorrhagic, cerebral sinovenous thrombosis). Preterm infants (< 28 weeks gestation) and neonates with germinal matrix/intraventricular hemorrhage were excluded. Descriptive statistics were performed to compare stroke sub-types, demographic and pregnancy-related risk factors. Results: We enrolled 103 cases, 69 newborn and 34 maternal. Median maternal age was 29 years (IQR 25-36) for the maternal group and 32 years (IQR 30-36) for the neonatal group. Women with maternal strokes were more likely to have pre-eclampsia (15%) and migraine (44%). Compared to maternal strokes, newborn strokes occurred more often in women from urban locations, were ischemic, and were less likely to be associated with alcohol use and pre-eclampsia during pregnancy (Table). Conclusion: In this novel registry, we describe factors that impact both maternal and newborn stroke. Urban area of residence and maternal age > 30 were associated with newborn strokes, while migraine and pre-eclampsia were strongly associated with maternal strokes. Differing mechanisms and stroke subtypes may explain these differences. In addition, lack of access to healthcare in certain geographical locations may lead to under-identification. Further research is needed to determine if these results are consistent when applied to a larger sample size.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".