Infective Endocarditis in Pregnancy: Unveiling the Challenges, Outcomes, and Strategies for Management
Bibliographic record
Abstract
Abstract Infective endocarditis (IE) is a serious and potentially fatal infection affecting cardiac endothelium and valves, with a significant increase in its incidence. This comprehensive review aims to discuss the challenges of diagnosing and managing IE during pregnancy, highlighting the absence of research and randomized clinical trials. Despite its low occurrence, IE in pregnancy is associated with significant maternal and fetal mortality rates, often complicated by prematurity. This review covers the physiological changes during pregnancy that can mask the symptoms of IE and the epidemiological shift in risk factors, including the rise in opioid addiction and the use of cardiac devices. It also sheds light on the specific microorganisms responsible for most IE cases. This paper involved a detailed search of PubMed databases, focusing on studies related to IE in pregnant patients, including those addressing fetal and maternal outcomes. It highlights the diagnostic challenges posed by the physiological changes in pregnancy, the impact of IE on maternal and fetal health, and the lack of specific treatment guidelines for pregnant women. We stress on the importance of a multidisciplinary approach to care, aiming to enhance early diagnosis, effective patient care strategies, and overall outcomes for this vulnerable population. Finally, our findings underscore the need for more research and the development of evidence-based guidelines to improve the management of IE in pregnancy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".