Massive haemorrhage in liver transplantation: Consequences, prediction and management
Bibliographic record
Abstract
From its inception the success of liver transplantation has been associated with massive blood loss. Massive transfusion is classically defined as > 10 units of red blood cells within 24 h, but describing transfusion rates over a shorter period of time may reduce the potential for survival bias. Both massive haemorrhage and transfusion are associated with increased risk of mortality and morbidity (need for dialysis/surgical site infection) following liver transplantation although causality is difficult to prove due to the observational design of most trials. The blood loss associated with liver transplantation is multifactorial. Portal hypertension secondary to cirrhosis results in extensive collateral circulation, which can bleed during hepatectomy particular if portal pressures are increased. Avoiding volume loading and maintenance of a low central venous pressure together with the use of vasopressors have been shown to reduce blood loss and transfusion during liver transplantation, but may increase the risk of renal impairment post-operatively. Coagulation defects may be present pre-transplant, but haemostasis is often re-balanced due to a deficit in both pro- and anti-coagulation factors. Further derangement of haemostasis may develop in the anhepatic and neohepatic phases due to absent hepatic metabolic function, hyperfibrinolysis and platelet sequestration in the donor liver. Point-of-care tests of coagulation such as the viscoelastic tests rotation thromboelastometry/thromboelastometry allow and more accurate and rapid assessment of these derangements in coagulation and guide the use of factor replacement and antifibrinolytics. Transfusion protocols guided by these tests have been shown to reduce transfusion rates compared with conventional coagulation tests, but have not shown improvements in mortality or morbidity. Pre-operative factors associated with massive transfusion include previous surgery, re-do transplantation, the aetiology and severity of liver disease. Intra-operatively the use of piggy-back technique and avoiding veno-veno bypass has been shown to reduced blood loss.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".