Bibliographic record
Abstract
Non-alcoholic fatty liver disease(NAFLD) is an important health problem worldwide. NAFLD encompasses a histological spectrum ranging from bland liver steatosis to severe steatohepatitis(nonalcoholic steatohepatitis, NASH) with the potential of progressing to cirrhosis and its associated morbidity and mortality. NAFLD is thought to be the hepatic manifestation of insulin resistance(or the metabolic syndrome); its prevalence is increasing worldwide in parallel with the obesity epidemic. In many developed countries, NAFLD is the most common cause of liver disease and NASH related cirrhosis is currently the third most common indication for liver transplantation. NASH related cirrhosis is anticipated to become the leading indication for liver transplantation within the next one or two decades. In this review, we discuss how liver transplantation is affected by NAFLD, specifically the following:(1) the increasing need for liver transplantation due to NASH;(2) the impact of the increasing prevalence of NAFLD in the general population on the quality of deceased and live donor livers available for transplantation;(3) the long term graft and patient outcomes after liver transplantation forNASH,and finally;and(4)the de novo occurrence of NAFLD/NASH after liver transplantation and its impact on graft and patient outcomes.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".