AASLD AST Practice Guideline on adult liver transplantation: Diagnosis and management of graft-related complications
Bibliographic record
Abstract
BACKGROUND AND AIM: Advances in immunosuppression and surgical techniques in liver transplantation (LT) have significantly improved patient outcomes, but donor utilization and access to LT remain a challenge. The past decade has seen significant strides in donor pool expansion with acceptable clinical outcomes, while improved patient selection and advances in the management of chronic liver disease and post-LT complications have promoted better allograft health. This document aims to provide an evidence-based guideline on the management of graft complications, immunosuppression, graft rejection and recurrent disease in adult LT recipients. METHODS: A multidisciplinary writing group of experts (transplant hepatologists, surgeons, pathologist, and psychiatrist) was tasked to formulate clinical questions (in PICO format) that arise during routine management of adult LT recipients. The writing group reviewed the literature, generated guideline recommendations and rated the level of evidence for each recommendation based on the Oxford Center for Evidence-Based Medicine. The group categorized the strength of recommendations based on the level of evidence, risk -benefit ratio, and patient preferences. CONCLUSION: While robust clinical trial data provide strong guidance on some aspects of graft management in LT such as machine perfusion and immunosuppression efficacy and safety, a significant component of graft management is derived from retrospective cohort data, extrapolation of data from other solid organ transplants, or expert opinion, including the treatment of antibody-mediated rejection. Finally, further investigation is needed to truly optimize the management of the liver allograft, including the prevention of recurrent alcohol-associated, metabolic dysfunction-associated steatotic and immune-mediated liver diseases.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".