Abstract T MP43: Patient Characteristics and Outcomes in Pregnancy-Related Ischemic Stroke
Bibliographic record
Abstract
Background: Ischemic Stroke (IS) is a rare but serious event during pregnancy or the postpartum period. We compared the characteristics and outcomes of pregnant vs. non-pregnant women diagnosed with IS in the Get With The Guidelines Stroke Registry. Methods: We identified 24641 female patients aged 18-44 with IS, based on medical history or ICD-9 codes, from 2008-2013. Patient and hospital categorical variables were compared by Chi-square and continuous variables by Wilcoxon Rank-Sum. Stratified logistic regression assessed the effect of pregnancy on outcomes conditional on age and adjusted for patient and hospital characteristics. Results: There were 338 (1.4%) pregnant IS patients. Compared to non-pregnant patients, pregnant patients had fewer traditional stroke risk factors, were less often black, and were more likely to be insured by Medicaid, in a healthcare setting at stroke onset, and admitted to a stroke center. Both groups had similar initial mild stroke severity and exam findings most notable for weakness (Table). Discharge outcomes of in-hospital death (aOR 0.70, 95% CI 0.33-1.50), discharge to home (aOR 1.04, 95% CI 0.81-1.34), independent ambulation (aOR 1.03, 95% CI 0.79-1.34) or length of stay >4 days (aOR 1.27, 95% CI 0.96-1.68) did not differ between groups. Of the 145 cases where pregnancy stage was coded, 76 (52.4%) occurred postpartum and 65 (44.8%) antepartum, with no difference in discharge outcomes. Women with postpartum compared with antepartum IS were more likely to have hypertension, use antihypertensives pre-stroke, and have higher median initial post-stroke blood pressure. Conclusions: Pregnancy-related IS is uncommon and occurs in women with few traditional stroke risk factors, often within 6 weeks postpartum. Despite these differences, short term outcomes after stroke are similar to non-pregnant women. Further research is needed to determine if pregnancy, itself, independently contributes to stroke risk.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".