Living Donor Liver Transplantation for Alcohol-related Liver Disease: An Intention-to-treat Analysis
Bibliographic record
Abstract
BACKGROUND: Alcohol-associated liver disease (ALD) is the leading indication for liver transplantation (LT) in the Western world. Although 6 mo of abstinence is no longer a criterion for patients with ALD, the outcomes of living donor LT (LDLT) versus deceased donor LT (DDLT) are not well established. METHODSS: We performed an intention-to-treat analysis to evaluate the impact of listing and pursuing primary LDLT (pLDLT) compared with primary DDLT (pDDLT). The primary endpoint was overall survival from date of listing, evaluated using Cox regression (hazard ratios). RESULTS: Two hundred thirty-three patients with ALD were listed for LT, of which 27 (12%) were pLDLT. The overall median model for end-stage liver disease (MELD) score at listing was 20 and Na-MELD 24, a median abstinence of 4.5 mo, and 128 (55%) underwent transplantation. There was no statistically significant adjusted difference at 3-y overall survival between pLDLT versus pDDLT (adjusted hazard ratio [HR] 0.72; P = 0.550) and in the as-treated analysis (HR 1.22; P = 0.741). No patients were delisted in the pLDLT group, whereas 86 (42%) patients were delisted in the pDDLT group; primarily because of death (46 [50%]) and medical improvement (24 [28%]). Alcohol use since the time of listing was documented in 29 (13%) patients; immortal time bias adjusted analysis found no significant difference between pLDLT and pDDLT (adjusted HR 1.07; P = 0.900) and the as-treated analysis (HR 2.95; P = 0.130). CONCLUSIONS: Patients with ALD benefit from intention pLDLT with lower rates of waitlist dropout and delisting, attributable to mortality or medical deterioration, and should be encouraged to pursue this option.
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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 | 0.017 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".