Transient Cold Storage Prior to Normothermic Liver Perfusion May Facilitate Adoption of a Novel Technology
Bibliographic record
Abstract
Clinical adoption of normothermic machine perfusion (NMP) may be facilitated by simplifying logistics and reducing costs. This can be achieved by cold storage of livers for transportation to recipient centers before commencing NMP. The purpose of this study was to assess the safety and feasibility of post-static cold storage normothermic machine perfusion (pSCS-NMP) in liver transplantation. In this multicenter prospective study, 31 livers were transplanted. The primary endpoint was 30-day graft survival. Secondary endpoints included the following: peak posttransplant aspartate aminotransferase (AST), early allograft dysfunction (EAD), postreperfusion syndrome (PRS), adverse events, critical care and hospital stay, biliary complications, and 12-month graft survival. The 30-day graft survival rate was 94%. Livers were preserved for a total of 14 hours 10 minutes ± 4 hours 46 minutes, which included 6 hours 1 minute ± 1 hour 19 minutes of static cold storage before 8 hours 24 minutes ± 4 hours 4 minutes of NMP. Median peak serum AST in the first 7 days postoperatively was 457 U/L (92-8669 U/L), and 4 (13%) patients developed EAD. PRS was observed in 3 (10%) livers. The median duration of initial critical care stay was 3 days (1-20 days), and median hospital stay was 13 days (7-31 days). There were 7 (23%) patients who developed complications of grade 3b severity or above, and 2 (6%) patients developed biliary complications: 1 bile leak and 1 anastomotic stricture with no cases of ischemic cholangiopathy. The 12-month overall graft survival rate (including death with a functioning graft) was 84%. In conclusion, this study demonstrates that pSCS-NMP was feasible and safe, which may facilitate clinical adoption.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".