Optimal Anticoagulation for Pregnant Women with Mechanical Heart Valves
Bibliographic record
Abstract
The prothrombotic state of pregnancy increases the risk of thromboembolic complications and death in women with mechanical heart valves (MHVs). Although it is accepted that these women must be on therapeutic anticoagulation throughout pregnancy, competing maternal and fetal risks, as well as the lack of high-quality data from prospective studies, make the choice of the optimal method of anticoagulation challenging. Vitamin K antagonists (VKAs) are associated with fewer maternal complications, but conversely also the lowest live birth rates as well as warfarin-related embryopathy and fetopathy. Low-molecular-weight heparin (LMWH) does not cross the placenta and is associated with fewer fetal risks but more maternal complications. Sequential treatment involving VKAs in the second and third trimesters and either low-molecular-weight or unfractionated heparin in the first trimester, although appealing is still associated with maternal complications, especially around the time of bridging. As absolute equipoise of maternal versus fetal wellbeing is unlikely, patient preferences should be considered in decision making. A multidisciplinary team including hematologists, cardiologists, obstetric physicians, and high-risk obstetricians with expertise in the management of pregnant women with cardiac disease is required to optimize outcomes. Prospective studies are needed to determine the anticoagulant regimen for women with MHVs that provides optimal and acceptable maternal and fetal 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".