Decompensated cirrhosis but low MELD—Should we wait or refer for liver transplantation?
Bibliographic record
Abstract
Cirrhosis constitutes a significant global health burden with decompensation characterized by ascites, hepatic encephalopathy, as well as variceal hemorrhage. These decompensation features are independent predictors of mortality. Liver transplantation remains the definitive treatment for patients with cirrhosis. However, given that this is a limited resource, thus its allocation, using the MELD score, has to be judicious despite decompensation features affecting the patient's quality of life. Patients with grade 3 ascites or overt hepatic encephalopathy have significant mortality; therefore, in some instances, these decompensation features should be considered indications for liver transplantation despite low MELD. The majority of patients listed for liver transplantation have low MELD scores (≤15 points); and approximately half will die due to liver-related complications. Current evidence demonstrates a mortality reduction of ~40% with LT in those patients with a low MELD. Furthermore, new scores have been developed, such as the MELD 3.0, which incorporates female sex, albumin, and all the MELD-Na components (bilirubin, creatinine, international normalized ratio, and sodium), and the Gender-Equity Model for Liver Allocation, that includes bilirubin, international normalized ratio, and the Royal Free Hospital glomerular filtration rate, which have demonstrated improved discrimination. Lastly, to address the limited resource, living donor liver transplant has demonstrated a significant survival benefit in patients even at MELD-Na scores as low as 11, suggesting that life-years gained are similar to deceased-donor transplant. In this review, our goal is to present the frequency of patients listed for transplant with low MELD, and the limitation of using MELD in patients for liver transplantation. We will provide practical guidance on the management of common complications of cirrhosis and early consideration for liver transplant referral in patients with clinical decompensation and low MELD.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".