Coagulation Defects in the Cirrhotic Patient Undergoing Liver Transplantation
Bibliographic record
Abstract
In Brief Patients with cirrhosis undergoing liver transplantation have unique challenges with regard to the prevention and management of thrombosis and hemorrhage. Patients with cirrhosis have an unstable balance of the coagulation system due to defects in both prothrombotic and antithrombotic components. These changes make laboratory monitoring challenging, prophylaxis against bleeding and thrombosis controversial, and therapy for the same uncertain. When cirrhotic patients undergo liver transplantation, they frequently have significant transfusion requirements. Emerging evidence may help aid in predicting which recipients will have the greatest blood product requirements, but the ideal blood product regimen to support them through the surgical procedure remains elusive. After these patients receive a liver they are at risk for both venous and arterial thrombotic complications. Unique to liver transplantation is the possibility of acquiring an inherited defect in coagulation, most commonly leading to a predisposition to thrombosis. Further high-quality prospective studies focusing on the management of cirrhotic patients are needed to better guide clinicians. This review provides an overview of the pathophysiology of hemostasis in liver disease, highlights challenges in laboratory monitoring, and offers recommendations for management of bleeding and thrombosis in the liver transplant recipient.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".