Pursuing living donor liver transplantation improves outcomes of patients with autoimmune liver diseases: An intention-to-treat analysis
Bibliographic record
Abstract
Living donor liver transplantation (LDLT) offers the opportunity to decrease waitlist time and mortality for patients with autoimmune liver disease (AILD), autoimmune hepatitis, primary biliary cholangitis, and primary sclerosing cholangitis. We compared the survival of patients with a potential living donor (pLDLT) on the waitlist versus no potential living donor (pDDLT) on an intention-to-treat basis. Our retrospective cohort study investigated adults with AILD listed for a liver transplant in our program between 2000 and 2021. The pLDLT group comprised recipients with a potential living donor. Otherwise, they were included in the pDDLT group. Intention-to-treat survival was assessed from the time of listing. Of the 533 patients included, 244 (43.8%) had a potential living donor. Waitlist dropout was higher for the pDDLT groups among all AILDs (pDDLT 85 [29.4%] vs. pLDLT 9 [3.7%], p < 0.001). The 1-, 3-, and 5-year intention-to-treat survival rates were higher for pLDLT versus pDDLT among all AILDs (95.7% vs. 78.1%, 89.0% vs. 70.1%, and 87.1% vs. 65.5%, p < 0.001). After adjusting for covariates, pLDLT was associated with a 38% reduction in the risk of death among the AILD cohort (HR: 0.62, 95% CI: 0.42-0.93 [ p <0.05]), and 60% among the primary sclerosing cholangitis cohort (HR: 0.40, 95% CI: 0.22-0.74 [ p <0.05]). There were no differences in the 1-, 3-, and 5-year post-transplant survival between LDLT and DDLT (AILD: 95.6% vs. 92.1%, 89.9% vs. 89.4%, and 89.1% vs. 87.1%, p =0.41). This was consistent after adjusting for covariates (HR: 0.97, 95% CI: 0.56-1.68 [ p >0.9]). Our study suggests that having a potential living donor could decrease the risk of death in patients with primary sclerosing cholangitis on the waitlist. Importantly, the post-transplant outcomes in this population are similar between the LDLT and DDLT groups.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".