Assessment and Prediction of Cardiovascular Contributions to Severe Maternal Morbidity
Bibliographic record
Abstract
Severe maternal morbidity (SMM) refers to any unexpected outcome directly related to pregnancy and childbirth that results in both short-term delivery complications and long-term consequences to a women's health. This affects about 60,000 women annually in the United States. Cardiovascular contributions to SMM including cardiac arrest, arrhythmia, and acute myocardial infarction are on the rise, probably driven by changing demographics of the pregnant population including more women of extreme maternal age and an increased prevalence of cardiometabolic and structural heart disease. The utilization of SMM prediction tools and risk scores specific to cardiovascular disease in pregnancy has helped with risk stratification. Furthermore, health system data monitoring and reporting to identify and assess etiologies of cardiovascular complications has led to improvement in outcomes and greater standardization of care for mothers with cardiovascular disease. Improving cardiovascular disease-related SMM relies on a multipronged approach comprised of patient-level identification of risk factors, individualized review of SMM cases, and validation of risk stratification tools and system-wide improvements in quality of care. In this article, we review the epidemiology and cardiac causes of SMM, we provide a framework of risk prediction clinical tools, and we highlight need for organization of care to improve outcomes.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".