Temporal evolution of living donor liver transplantation survival—A United Network for Organ Sharing registry study
Bibliographic record
Abstract
Living donor liver transplantation (LDLT) is a curative treatment for various liver diseases, reducing waitlist times and associated mortality. We aimed to assess the overall survival (OS), identify predictors for mortality, and analyze differences in risk factors over time. Adult patients undergoing LDLT were selected from the United Network for Organ Sharing database from inception (1987) to 2023. The Kaplan-Meier method was used for analysis, and multivariable Cox proportional hazard models were conducted. In total, 7257 LDLT recipients with a median age of 54 years (interquartile range [IQR]: 45-61 years), 54% male, 80% non-Hispanic White, body mass index of 26.3 kg/m 2 (IQR: 23.2-30.0 kg/m 2 ), and model for end-stage liver disease score of 15 (IQR: 11-19) were included. The median cold ischemic time was 1.6 hours (IQR: 1.0-2.3 hours) with 88% right lobe grafts. The follow-up was 4.0 years (IQR: 1.0-9.2 years). The contemporary reached median OS was 17.0 years (95% CI: 16.1, 18.1 years), with the following OS estimates: 1 year 95%; 3 years 89%; 5 years 84%; 10 years 72%; 15 years 56%; and 20 years 43%. Nine independent factors associated with mortality were identified, with an independent improved OS in the recent time era (adjusted hazards ratio: 0.53; 95% CI: 0.39, 0.71). The median center-caseload per year was 5 (IQR: 2-10), with observed center-specific improvement of OS. LDLT is a safe procedure with excellent OS. Its efficacy has improved despite an increase of risk parameters, suggesting its limits are yet to be met.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".