Normothermic ex-vivo liver perfusion: where do we stand and where to reach?
Bibliographic record
Abstract
INTRODUCTION: Nowadays liver transplantation is considered as the treatment of choice, however, the scarcity of suitable donor organs limits the delivery of care to the end-stage liver disease patients leading to the death while on the waiting list. The advent of ex-situ normothermic machine perfusion (NMP) has emerged as an alternative to the standard organ preservation technique, static cold storage (SCS). The newer technique promises to not only restore the normal metabolic activity but also attempt to recondition the marginal livers back to the pristine state, which are otherwise more susceptible to ischemic injury and foster the poor post-transplant outcomes. Areas covered: An extensive search of all the published literature describing the role of NMP based device in liver transplantation as an alternative to SCS was made on MEDLINE, EMBASE, Cochrane, BIOSIS, Crossref, Scopus databases and clinical trial registry on 10 May 2018. Expert commentary: The main tenet of NMP is the establishment of the physiological milieu, which permits aerobic metabolism to continue through out the period of preservation and limits the effects of ischemia-reperfusion (I/R) injury. In addition, by assessing the various metabolic and synthetic parameters the viability and suitability of donor livers for transplantation can be determined. This important technological advancement has scored satisfactorily on the safety and efficacy parameters in preliminary clinical studies. The present review suggests that NMP can offer the opportunity to assess and safely utilize the marginal donor livers if deemed appropriate for the transplantation. However, ongoing trials will determine its full potential and further adoption.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".