A Comprehensive Review of Liver Allograft Fibrosis and Steatosis: From Cause to Diagnosis
Bibliographic record
Abstract
Despite advances in posttransplant care, long-term outcomes for liver transplant recipients remain unchanged. Approximately 25% of recipients will advance to graft cirrhosis and require retransplantation. Graft fibrosis progresses in the context of de novo or recurrent disease. Recurrent hepatitis C virus infection was previously the most important cause of graft failure but is now curable in the majority of patients. However, with an increasing prevalence of obesity and diabetes and nonalcoholic fatty liver disease as the most rapidly increasing indication for liver transplantation, metabolic dysfunction-associated liver injury is anticipated to become an important cause of graft fibrosis alongside alloimmune hepatitis and alcoholic liver disease. To better understand the landscape of the graft fibrosis literature, we summarize the associated epidemiology, cause, potential mechanisms, diagnosis, and complications. We additionally highlight the need for better noninvasive methods to ameliorate the management of graft fibrosis. Some examples include leveraging the microbiome, genetic, and machine learning methods to address these limitations. Overall, graft fibrosis is routinely seen by transplant clinicians, but it requires a better understanding of its underlying biology and contributors that can help inform diagnostic and therapeutic practices.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".