Systematic Review and Meta-Analysis on the Impact of Thrombolytic Therapy in Liver Transplantation Following Donation after Circulatory Death
Bibliographic record
Abstract
Aim: The livers from DCD (donation after cardiac death) donations are often envisaged as a possible option to bridge the gap between the availability and increasing demand of organs for liver transplantation. However, DCD livers possess a heightened risk for complications and represent a formidable management challenge. The aim of this study was to evaluate the effects of thrombolytic flush in DCD liver transplantation. Methods: An extensive search of the literature database was made on MEDLINE, EMBASE, Cochrane, Crossref, Scopus databases, and clinical trial registry on 20 September 2018 to assess the role of thrombolytic tissue plasminogen activator (tPA) flush in DCD liver transplantation. Results: A total of four studies with 249 patients in the tPA group and 178 patients in the non-tPA group were included. The pooled data revealed a significant decrease in ischemic-type biliary lesions (ITBLs) (P = 0.04), re-transplantation rate (P = 0.0001), and no increased requirement of blood transfusion (P = 0.16) with a better one year graft survival (P = 0.02). Conclusions: To recapitulate, tPA in DCD liver transplantation decreased the incidence of ITBLs, re-transplantation and markedly improved 1-year graft survival, without any increased risk for blood transfusion, hence it has potential to expand the boundaries of DCD liver transplantation.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".