Living Donor Availability Improves Patient Survival in a North American Center
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to assess the impact of having a living donor on waitlist outcomes and overall survival through an intention-to-treat analysis. BACKGROUND: Living-donor liver transplantation (LDLT) offers an alternative to deceased donation in the face of organ shortage. An as-treated analysis revealed that undergoing LDLT, compared with staying on the waiting list, is associated with improved survival, even at Model for End-stage Liver Disease-sodium (MELD-Na) score of 11. METHODS: Liver transplant candidates listed at the Ajmera Transplant Centre (2000-2021) were categorized as pLDLT (having a potential living donor) or pDDLT (without a living donor). Employing Cox proportional-hazard regression with time-dependent covariates, we evaluated pLDLT's impact on waitlist dropout and overall survival through a risk-adjusted analysis. RESULTS: Of 4124 candidates, 984 (24%) had potential living donors. The pLDLT group experienced significantly lower overall waitlist dropouts (5.2% vs 34.4%, P <0.001) and mortality (3.8% vs 24.4%, P <0.001) compared with the pDDLT group. Possessing a living donor correlated with a 26% decline in the risk of waitlist dropout (adjusted hazard ratio=0.74, 95% CI: 0.55-0.99, P =0.042). The pLDLT group also demonstrated superior survival outcomes at 1 year (84.9% vs 80.1%), 5 years (77.6% vs 61.7%), and 10 years (65.6% vs 52.9%) from listing (log-rank P <0.001) with a 35% reduced risk of death (adjusted hazard ratio=0.65, 95% CI: 0.56-0.76, P <0.001). Moreover, the predicted hazard ratios consistently remained <1 across the MELD-Na range of 11 to 26. CONCLUSIONS: Having a potential living donor significantly improves survival in end-stage liver disease patients, even with MELD-Na scores as low as 11. This emphasizes the need to promote awareness and adoption of LDLT in liver transplant programs worldwide.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".