Superior Long‐Term Outcomes of Adult Living Donor Liver Transplantation: A Cumulative Single‐Center Cohort Study With 20 Years of Follow‐Up
Bibliographic record
Abstract
Living donor liver transplantation (LDLT) is an attractive alternative to deceased donor liver transplantation (DDLT). Although both modalities have similar short-term outcomes, long-term outcomes are not well studied. We compared the 20-year outcomes of 668 adults who received LDLT with1596 DDLTs at the largest liver transplantation (LT) program in Canada. Recipients of LDLT were significantly younger and more often male than DDLT recipients (P < 0.001). Autoimmune diseases were more frequent in LDLT, whereas viral hepatitis and alcohol-related liver disease were more frequent in DDLT. LDLT recipients had lower Model for End-Stage Liver Disease scores (P = 0.008), spent less time on the waiting list (P < 0.001), and were less often inpatients at the time of LT (P < 0.001). In a nonadjusted analysis, 1-year, 10-year, and 20-year patient survival rates were significantly higher in LDLT (93%, 74%, and 56%, respectively) versus DDLT (91%, 67%, and 46%, respectively; log-rank P = 0.02) as were graft survival rates LDLT (91%, 67%, and 50%, respectively) versus (90%, 65%, and 44.3%, respectively, for DDLT; log-rank P = 0.31). After multivariable adjustment, LDLT and DDLT were associated with a similar hazard of patient and graft survival. Our data of 20 years of follow-up of LDLT from a single, large Western center demonstrates excellent long-term outcomes for recipients of LDLT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".