Normothermic and subnormothermic ex-vivo liver perfusion in liver transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: In the current era of extreme organ shortage, warm (subnormothermic and normothermic) ex-vivo liver perfusion has emerged as a novel strategy to recover marginal organs and increase the organ pool. Over the last decade, significant progress in the field has taken this technology from bench to bedside. This review will cover the most relevant contributions to the field in 2015. RECENT FINDINGS: Several groups made significant advances in warm ex-vivo liver perfusion for optimizing preservation of liver grafts. With transition to clinical use underway, significant interest has focused on exploring the safety and feasibility of the technique. Other areas of exploration included novel perfusates and rewarming strategies. This review will also summarize the most recent advances in the clinical setting. SUMMARY: Warm ex-vivo liver perfusion has established itself as a novel approach for the preservation of liver grafts for transplantation. Although the optimal perfusion conditions and techniques have not been established, the safety of this technique has been demonstrated in preclinical and clinical studies. Thus far, most investigation has focused on the rescue of marginal grafts. However, further development in the field has the potential to yield novel graft interventions and modification.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".