Challenges With the Implementation of Machine Perfusion in Clinical Liver Transplantation
Bibliographic record
Abstract
Dynamic organ preservation is a relatively old technique which has regained significant interest in the last decade. Machine perfusion (MP) techniques are applied in various fields of solid organ transplantation today. The first clinical series of ex situ MP in liver transplantation was presented in 2010. Since then, the number of research and clinical applications has substantially increased. Despite the notable beneficial effect on organ quality and recipient outcome, MP is still not routinely used in liver transplantation. Based on the enormous need to better preserve organs and the subsequent demand to continuously innovate and develop perfusion equipment further, this technology is also beneficial to test and deliver future therapeutic strategies to livers before implantation. This article summarizes the various challenges observed during the current shift from static to dynamic liver preservation in the clinical setting. The different organ perfusion strategies are discussed first, together with ongoing clinical trials and future study design. The current status of research and the impact of costs and regulations is highlighted next. Factors contributing to costs and other required resources for a worldwide successful implementation and reimbursement are presented third. The impact of research on cost-utility and effectivity to guide the tailored decision-making regarding the optimal perfusion strategy is discussed next. Finally, this article provides potential solutions to the challenging field of innovation in healthcare considering the various social and economic factors and the role of clinical, regulatory, and financial stakeholders worldwide.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".