The incidence of pregnancy-related stroke: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Stroke risk is increased during pregnancy, but estimates of pregnancy-related stroke incidence vary widely. Aims A systematic review and meta-analysis was conducted to assess the incidence of stroke during pregnancy and the puerperium. Ovid Medline, EMBASE, and ISI Web of Science were searched for studies published between 1990 and January 2017 reporting stroke incidence during pregnancy and postpartum, from defined pregnancy populations. Pooled analyses were conducted using a random effects approach and expressed as an incidence rate per 100,000 pregnancies, with 95% confidence intervals. Subgroup analyses of stroke type and timing were conducted. Summary of review Eleven studies met inclusion criteria. Variation in estimated rates was noted based on geography and study methodology. The pooled crude rate of pregnancy-related stroke was 30.0 per 100,000 pregnancies (95% confidence interval 18.8-47.9). The pooled crude rates from nonhemorrhagic stroke (arterial and cerebral venous sinus thrombosis) were 19.9 (95% confidence interval 10.7-36.9) and from hemorrhage 12.2 (95% confidence interval 6.4-23.2) per 100,000 pregnancies. For studies separately reporting cerebral venous sinus thrombosis, the rates were roughly equal between ischemic stroke (12.2, 95% confidence interval 6.7-22.2), cerebral venous sinus thrombosis (9.1, 95% confidence interval 4.3-18.9), and hemorrhage (12.2, 95% confidence interval 6.4-23.2). The crude stroke rate for antenatal/perinatal stroke was 18.3 (95% confidence interval 11.9-28.2), and for postpartum stroke was 14.7 (95% confidence interval 8.3-26.1). Conclusions Stroke affects 30.0 per 100,000 pregnancies, with ischemia, cerebral venous sinus thrombosis, and hemorrhage causing roughly equal numbers and with highest risk peripartum and postpartum. Organized approaches to the management of this high-risk population, informed by existing evidence from stroke and obstetrical care are needed.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".