MATERNAL PREGNANCY OUTCOMES IN WOMEN WITH CARDIOMYOPATHY: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Introduction: Pregnancy poses significant cardiovascular challenges, affecting 1 to 4 of pregnancies due to maternal heart disease. Cardiomyopathies, leading indirect causes of maternal mortality globally, raise concerns during pregnancy, despite limited data on their impact and the accuracy of predictive scores like Carpreg and Zahara. Maternal heart disease, comprising hypertensive disorders and cardiomyopathies, presents substantial risks during pregnancies, driving increased maternal mortality rates. The evolving landscape, marked by improved survival in congenital heart disease and delayed pregnancies with associated comorbidities, necessitates a more nuanced understanding and comprehensive predictive models for tailored care during pregnancy. Methods: The researchers in this study followed the 2020 Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines to ensure that their work met the required standards. This was done to ensure the precision and reliability of the conclusions derived from the research. Result: Our search produced 15 results. After looking at the titles and summaries, we found 11 papers that fit our criteria. At first, we excluded several articles because they were written in review and case report style. But after reading the full papers carefully, we included five papers in our final analysis. These papers included prospective and retrospective studies. Conclusion: The investigation of pregnant women in Canadian obstetric centers unveiled that cardiac issues affect 16 of pregnancies, primarily with arrhythmias and heart failure, even in lower-risk cases, with complications emerging before birth. The study's precision in evaluating pregnancy-related cardiac risks, leading to a new CARPREG II risk index and emphasizing tailored surveillance and timely referrals, was notable. The ROPAC registry highlighted higher maternal mortality rates, stressing vigilant management throughout pregnancy and after delivery, emphasizing the necessity for specialized care and research advancements in this field.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".