Steatosis in Liver Transplantation: Current Limitations and Future Strategies
Bibliographic record
Abstract
In parallel with the pandemic of obesity and diabetes, the prevalence of nonalcoholic fatty liver disease has progressively increased. Nonalcoholic steatohepatitis (NASH), a subtype of nonalcoholic fatty liver disease has also augmented considerably being currently cirrhosis due to NASH the second indication for liver transplantation in the United States. Innovative treatments for NASH have shown promising results in phase 2 studies and are being presently evaluated in phase 3 trials. On the other hand, the high mortality on the liver transplant waitlist and the organ shortage has obligated the transplant centers to consider suboptimal grafts, such as steatotic livers for transplantation. Fatty livers are vulnerable to preservation injury resulting in a higher rate of primary nonfunction, early allograft dysfunction and posttransplant vascular and biliary complications. Macrosteatosis of more than 30% in fact is an independent risk factor for graft loss. Therefore, it needs to be considered into the risk assessment scores. Growing evidence supports that moderate and severe macrosteatotic grafts can be successfully used for liver transplantation with careful recipient selection. Protective strategies, such as machine-based perfusion have been developed in experimental setting to minimize preservation-related injury and are now on the verge to move into the clinical implementation. This review focuses on the current and potential future treatment of NASH and the clinical practice in fatty liver transplantation, highlights its limitations and optimal allocation, and summarizes the advances of experimental protective strategies, and their potential for clinical application to increase the acceptance and improve the outcomes after liver transplantation with high-grade steatotic livers.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".