Racial and Ethnic Disparities in Liver Transplantation for Alcohol-associated Liver Diseases in the United States
Bibliographic record
Abstract
BACKGROUND: Emerging data suggest disparities exist in liver transplantation (LT) for alcohol-associated liver disease (ALD). As the incidence of ALD increases, we aimed to characterize recent trends in ALD LT frequency and outcomes, including racial and ethnic disparities. METHODS: Using United Network for Organ Sharing/Organ Procurement and Transplantation Network data (2015 through 2021), we evaluated LT frequency, waitlist mortality, and graft survival among US adults with ALD (alcohol-associated hepatitis [AH] and alcohol-associated cirrhosis [AAC]) stratified by race and ethnicity. We used adjusted competing-risk regression analysis to evaluate waitlist outcomes, Kaplan-Meier analysis to illustrate graft survival, and Cox proportional hazards modeling to identify factors associated with graft survival. RESULTS: There were 1211 AH and 26 526 AAC new LT waitlist additions, with 970 AH and 15 522 AAC LTs performed. Compared with non-Hispanic White patients (NHWs) with AAC, higher hazards of waitlist death were observed for Hispanic (subdistribution hazard ratio [SHR] = 1.23, 95% confidence interval [CI]: 1.16-1.32), Asian (SHR = 1.22, 95% CI:1. 01-1.47), and American Indian/Alaskan Native (SHR = 1.42, 95% CI: 1.15-1.76) candidates. Similarly, significantly higher graft failures were observed in non-Hispanic Black (HR = 1.32, 95% CI: 1.09-1.61) and American Indian/Alaskan Native (HR = 1.65, 95% CI: 1.15-2.38) patients with AAC than NHWs. We did not observe differences in waitlist or post-LT outcomes by race or ethnicity in AH, although analyses were limited by small subgroups. CONCLUSIONS: Significant racial and ethnic disparities exist for ALD LT frequency and outcomes in the United States. Compared with NHWs, racial and ethnic minorities with AAC experience increased risk of waitlist mortality and graft failure. Efforts are needed to identify determinants for LT disparities in ALD that can inform intervention strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".