Does machine perfusion improve immediate and short‐term outcomes by enhancing graft function and recipient recovery after liver transplantation? A systematic review of the literature, meta‐analysis and expert panel recommendations
Bibliographic record
Abstract
BACKGROUND: Recent evidence supports the use of machine perfusion technologies (MP) for marginal liver grafts. Their effect on enhanced recovery, however, remains uncertain. OBJECTIVES: To identify areas in which MP might contribute to an ERAS program and to provide expert panel recommendations. DATA SOURCES: Ovid MEDLINE, Embase, Scopus, Google Scholar, and Cochrane Central. METHODS: Systematic review and meta-analysis following PRISMA guidelines and recommendations using the GRADE approach. CRD42021237713 RESULTS: Both hypothermic (HMP) and normothermic (NMP) machine perfusion demonstrated significant benefits in preventing postreperfusion syndrome (PRS) (HMP OR .33, .15-.75 CI; NMP OR .51, .29-.90 CI) and early allograft dysfunction (EAD) (HMP OR .51, .35-.75 CI; NMP OR .66, .45-.97 CI), while shortening LOS (HMP MD -3.9; NMP MD -12.41). Only NMP showed a significant decrease in the length of ICU stay (L-ICU) (MD -7.07, -8.76; -5.38 CI), while only HMP diminishes the likelihood of major complications. Normothermic regional perfusion (NRP) reduces EAD (OR .52, .38-.70 CI) and primary nonfunction (PNF) (OR .51, .27-.98 CI) without effect on L-ICU and LOS. CONCLUSIONS: The use of HMP decreases PRS and EAD, specifically for marginal grafts. This is supported by a shorter LOS and a lower rate of major postoperative complications (QOE; moderate | Recommendation; Strong). NMP reduces the incidence of PRS and EAD with associated shortening in L-ICU for both DBD and DCD grafts (QOE; moderate | Recommendation; High) This technology also shortens the length of hospital stay (QOE; low | Recommendation; Strong). NRP decreases the likelihood of EAD (QOE; moderate) and the risk of PNF (QOE; low) when compared to both DBD and SRR-DCD grafts preserved in SCS. (Recommendation; Strong).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".