Delisting From Liver Transplant List for Improvement and Recompensation Among Decompensated Patients at One Year
Bibliographic record
Abstract
INTRODUCTION: Data are limited regarding etiology-specific trends for delisting and recompensation for liver disease improvement among liver transplantation (LT)-listed candidates in the United States. METHODS AND RESULTS: A retrospective cohort (2002-2022) using United Network of Organ Sharing database examined etiology-specific trends for delisting and recompensation due to liver disease improvement among candidates listed for LT. Of 120,451 listings in adults, 34,444 (2002-2008), 38,296 (2009-2015), 47,711 (2016-2022) were analyzed. A total of 7,196 (6.2%) were delisted for liver disease improvement, with 5.6%, 7.2%, and 5.3% in 3 respective time periods, Armitage trend P < 0.001. Delisting for improvement of liver disease was 8.1%, 5.8%, 4.0%, 3.9%, and 3.1% among listings for alcohol-associated liver disease (ALD n = 41,647), hepatitis C virus infection (HCV n = 38,797), autoimmune (n = 12,131), metabolic-associated steatohepatitis (MASH n = 22,162), hepatitis B virus infection (HBV n = 3,027), and metabolic liver disease (MLD n = 2,687), respectively. One thousand one hundred twenty-two (15.6% or 0.9%) were delisted for improvement at 1 year with cumulative incidence competing for waitlist mortality and receipt of LT of 1.18, 1.17, 0.64, 0.59, 0.50, and 0.34 for ALD, HBV, HCV, MASH, MLD, and autoimmune, respectively. In a fine and gray model, compared with metabolic, subhazard ratio (95% confidence interval) on delisting at 1 year was 3.47 (31.6-3.81) and 3.44 (2.96-3.99), P < 0.001, for ALD and for HBV, respectively. Of 7,196 delisting for improvement, 567 of 5,750 (9.9%) decompensated at listing had recompensation, 19.5% for HBV, 16.6% for MLD and autoimmune, 9.9% ALD, 8.6% for HCV, and 6.9% for MASH. In a logistic regression model among delisted candidates for improved liver disease, HBV vs MASH etiology was associated with recompensation, 2.37 (1.40-4.03), P < 0.001. DISCUSSION: ALD and HBV are most frequent etiologies for delisting due to liver disease improvement. About 10% of delisted patients develop recompensation, with HBV etiology most likely to recompensate. Models and biomarkers are needed to identify these candidates for optimal use of deceased donor livers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".