Recent advances in the field of warm ex-vivo liver perfusion
Bibliographic record
Abstract
PURPOSE OF REVIEW: Organ shortage remains a major obstacle for liver transplantation, resulting in an increased mortality on the liver transplant waiting list. The usage of extended criteria donors (ECD) is a strategy to increase the number of available donor organs, however, with the risk of a higher rate of posttransplant graft dysfunction. Novel preservation strategies, such as warm ex-vivo liver perfusion, could improve the outcome of liver transplantation with ECD grafts. The present review summarizes the advances in the field of warm ex-vivo liver perfusion over the last 12 months. RECENT FINDINGS: The feasibility and safety of warm ex-vivo liver perfusion has been determined in several single center clinical trials. Furthermore, a large phase III multicenter trial demonstrated decreased liver injury and improved graft function in warm perfused versus cold stored grafts. New strategies for graft assessment and modification during machine perfusion have been evaluated with promising results. SUMMARY: Warm ex-vivo liver perfusion has been successfully translated into the clinical setting. Recent research is focusing on graft assessment and graft modification during machine perfusion.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".