Changing liver utilization and discard rates in clinical transplantation in the ex-vivo machine preservation era
Bibliographic record
Abstract
Liver transplantation is a well-established treatment for many with end-stage liver disease. Unfortunately, the increasing organ demand has surpassed the donor supply, and approximately 30% of patients die while waiting for a suitable liver. Clinicians are often forced to consider livers of inferior quality to increase organ donation rates, but ultimately, many of those organs end up being discarded. Extensive testing in experimental animals and humans has shown that ex-vivo machine preservation allows for a more objective characterization of the graft outside the body, with particular benefit for suboptimal organs. This review focuses on the history of the implementation of ex-vivo liver machine preservation and how its enactment may modify our current concept of organ acceptability. We provide a brief overview of the major drivers of organ discard (age, ischemia time, steatosis, etc.) and how this technology may ultimately revert such a trend. We also discuss future directions for this technology, including the identification of new markers of injury and repair and the opportunity for other ex-vivo regenerative therapies. Finally, we discuss the value of this technology, considering current and future donor characteristics in the North American population that may result in a significant organ discard.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".