Normothermic and subnormothermic ex-vivo liver perfusion in liver transplantation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it