Changing Trends in Liver Transplantation: Challenges and Solutions
Bibliographic record
Abstract
Despite improvements in postliver transplant outcomes through refinements in perioperative management and surgical techniques, several changing trends in liver transplantation have presented challenges. Mortality on the waitlist remains high. In the United States, Europe, and the United Kingdom, there is an increasing need for liver transplantation, primarily as a result of increased incidence of nonalcoholic steatohepatitis-related cirrhosis and cancer indications. Meanwhile, donor suitability has decreased, as donors are often older and have more comorbidities. Despite a mismatch between organ need and availability, many organs are discarded. Notwithstanding this, many solutions have been developed to overcome these challenges. Innovative techniques in allograft preservation, viability assessment, and reconditioning have allowed the use of suboptimal organs with adequate results. Refinements in surgical procedures, including live donor liver transplantations, have increased the organ pool and are decreasing the time and mortality on the waitlist. Despite many challenges, a similar number of solutions and prospects are on the horizon. This review seeks to explore the changing trends and challenges in liver transplantation and highlight possible solutions and future directions.
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.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".