Optimizing patients with non-alcoholic fatty liver disease pre-transplant
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in Western countries. Non-alcoholic steatohepatitis (NASH), which is the progressive counterpart of the disease, is becoming the leading indication for liver transplantation in North America. Owing to the lack of symptoms, NASH is often an incidental diagnosis, resulting in a significant proportion of patients being diagnosed when advanced liver disease has already developed. NAFLD has recently been characterized as the hepatic manifestation of metabolic syndrome. Consequently, it is a multisystem disease that often co-exists with several other conditions, such as obesity, diabetes, cardiovascular diseases, and extra-hepatic malignancy, which have an impact on selection of transplant recipients. The complexity of diagnostic approach, need for multidisciplinary clinical management, and lack of a specific treatment further complicate the picture of this extremely prevalent liver condition. NAFLD patients with advanced liver disease should be considered for early referral to liver transplant clinics for careful metabolic and cardiovascular risk stratification because they have worse survival rates after liver transplantation than other patients with chronic liver disease. Early referral will also facilitate optimization of metabolic comorbidities before proceeding with transplantation. This review provides an overview of strategies to identify patients with advanced NAFLD, with an emphasis on the management of associated comorbidities and optimal timing of pre-transplant evaluation. Other topics that have been shown to affect recipient optimization, such as the role of lifestyle changes and bariatric surgery in the management of obesity, as well as sarcopenia in decompensated NASH-related cirrhosis, are addressed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".