Patient Characteristics and Outcomes After Hemorrhagic Stroke in Pregnancy
Bibliographic record
Abstract
BACKGROUND: Hospitalizations for pregnancy-related stroke are rare but increasing. Hemorrhagic stroke (HS), ie, subarachnoid hemorrhage and intracerebral hemorrhage, is more common than ischemic stroke in pregnant versus nonpregnant women, reflecting different phenotypes or risk factors. We compared stroke risk factors and outcomes in pregnant versus nonpregnant HS in the Get With The Guidelines-Stroke Registry. METHODS AND RESULTS: Using medical history or International Classification of Diseases-Ninth Revision codes, we identified 330 pregnant and 10 562 nonpregnant female patients aged 18 to 44 years with HS in Get With The Guidelines-Stroke (2008-2014). Differences in patient and care characteristics were compared by χ(2) or Fisher exact test (categorical variables) or Wilcoxon rank-sum (continuous variables) tests. Conditional logistic regression assessed the association of pregnancy with outcomes conditional on categorical age and further adjusted for patient and hospital characteristics. Pregnant versus nonpregnant HS patients were younger with fewer pre-existing stroke risk factors and medications. Pregnant versus nonpregnant subarachnoid hemorrhage patients were less impaired at arrival, and less than half met blood pressure criteria for severe preeclampsia. In-hospital mortality was lower in pregnant versus nonpregnant HS patients: adjusted odds ratios (95% CI) for subarachnoid hemorrhage 0.17 (0.06-0.45) and intracerebral hemorrhage 0.57 (0.34-0.94). Pregnant subarachnoid hemorrhage patients also had a higher likelihood of home discharge (2.60 [1.67-4.06]) and independent ambulation at discharge (2.40 [1.56-3.70]). CONCLUSIONS: Pregnant HS patients are younger and have fewer risk factors than their nonpregnant counterparts, and risk-adjusted in-hospital mortality is lower. Our findings suggest possible differences in underlying disease pathophysiology and challenges to identifying at-risk patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".