Optimizing Livers for Transplantation Using Machine Perfusion versus Cold Storage in Large Animal Studies and Human Studies: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Liver allograft preservation frequently involves static cold storage (CS) and machine perfusion (MP). With its increasing popularity, we investigated whether MP was superior to CS in terms of beneficial outcomes. METHODS: Human studies and large animal studies that optimized livers for transplantation using MP versus CS were assessed (PubMed/Medline/EMBASE). Meta-analyses were conducted for comparisons. Study quality was assessed according to the Newcastle-Ottawa quality assessment scale and SYRCLE's risk of bias tool. RESULTS: Nineteen studies were included. Among the large animal studies, lower levels of lactate dehydrogenase (SMD -3.16, 95% CI -5.14 to -1.18), alanine transferase (SMD -2.46, 95% CI -4.03 to -0.90), and hyaluronic acid (SMD -2.48, 95% CI -4.21 to -0.74) were observed in SNMP-preserved compared to CS-preserved livers. NMP-preserved livers showing lower level of hyaluronic acid (SMD -3.97, 95% CI -5.46 to -2.47) compared to CS-preserved livers. Biliary complications (RR 0.45, 95% CI 0.28 to 0.73) and early graft dysfunction (RR 0.56, 95% CI 0.34 to 0.92) also significantly reduced with HMP preservation in human studies. No evidence of publication bias was found. CONCLUSIONS: MP preservation could improve short-term outcomes after transplantation compared to CS preservation. Additional randomized controlled trials (RCTs) are needed to develop clinical applications of MP preservation.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".