A case report: mechanical tricuspid valve thrombosis necessitating cardiac surgery during pregnancy
Bibliographic record
Abstract
BACKGROUND: Pregnant women with mechanical valves are considered a high-risk pregnancy. They carry an increased risk of both maternal and foetal complications. This includes maternal valve thrombosis, foetal embryopathy, and haemorrhage. Cardiac surgery is generally avoided during pregnancy, and is used when there are no other alternative options. Cardiopulmonary bypass (CPB) during pregnancy is associated with high foetal mortality. Maternal mortality in the setting of CPB however, is not dissimilar to a non-pregnant woman. CASE SUMMARY: We present the case of a 29-year-old woman with Ebstein's anomaly who developed thrombosis of her mechanical tricuspid valve at 4 weeks' gestation. This was suspected to be likely due to sub-therapeutic anticoagulation at the time of presentation. She underwent a tricuspid valve replacement during the first trimester of pregnancy after failing medical therapy, with overall favourable maternal and foetal outcomes. DISCUSSION: Valve thrombosis during pregnancy is a devastating complication. There is limited data surrounding the best management strategy of valve thrombosis in pregnancy. Cardiac surgery with CPB is reserved for cases refractory to appropriate medical therapy. Though maternal mortality is largely unaffected, foetal mortality with CPB remains high. The management of pregnant women undergoing CPB is unique and extremely challenging. It requires a meticulous, multidisciplinary approach to improve overall 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".