Long-term outcomes after normothermic machine perfusion in liver transplantation—Experience at a single North American center
Bibliographic record
Abstract
Normothermic machine perfusion (NMP) has emerged as a valuable tool in the preservation of liver allografts before transplantation. Randomized trials have shown that replacing static cold storage (SCS) with NMP reduces allograft injury and improves graft utilization. The University of Alberta's liver transplant program was one of the early adopters of NMP in North America. Herein, we describe our 7-year experience applying NMP to extend preservation time in liver transplantation using a "back-to-base" approach. From 2015 to 2021, 79 livers were transplanted following NMP, compared with 386 after SCS only. NMP livers were preserved for a median time of minutes compared with minutes in the SCS cohort (P < .0001). Despite this, we observed significantly improved 30-day graft survival (P = .030), although there were no differences in long-term patient survival, major complications, or biliary or vascular complications. We also found that although SCS time was strongly associated with increased graft failure at 1 year in the SCS cohort (P = .006), there was no such association among NMP livers (P = .171). Our experience suggests that NMP can safely extend the total preservation time of liver allografts without increasing complications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".