Ex vivo machine perfusion: current applications and future directions in liver transplantation
Bibliographic record
Abstract
BACKGROUND: Liver transplantation is the only curative treatment option for end-stage liver disease; however, its use remains limited due to a shortage of suitable organs. In recent years, ex vivo liver machine perfusion has been introduced to liver transplantation, as a means to expand the donor organ pool. PURPOSE: To present a systematic review of prospective clinical studies on ex vivo liver machine perfusion, in order to assess current applications and highlight future directions. METHODS: A systematic literature search of both PubMed and ISI web of science databases as well as the ClinicalTrials.gov registry was performed. RESULTS: Twenty-one articles on prospective clinical trials on ex vivo liver machine perfusion were identified. Out of these, eight reported on hypothermic, eleven on normothermic, and two on sequential perfusion. These trials have demonstrated the safety and feasibility of ex vivo liver machine perfusion in both standard and expanded criteria donors. Currently, there are twelve studies enrolled in the clinicaltrials.gov registry, and these focus on use of ex vivo perfusion in extended criteria donors and declined organs. CONCLUSION: Ex vivo liver machine perfusion seems to be a suitable strategy to expand the donor pool for liver transplantation and holds promise as a platform for reconditioning diseased organs.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".