Expanding the living donor pool using domino liver transplantation: a systematic review
Bibliographic record
Abstract
INTRODUCTION: To this day, a discrepancy exists between donor liver demand and supply. Domino liver transplantation (DLT) can contribute to increasing the number of donor livers available for transplantation. METHODS: The design of this systematic review was based on the Preferred Reporting Items for Systematic Reviews (PRISMA). A qualitative analysis of included studies was performed. Primary outcomes were mortality and peri- and postoperative complications related to DLT. RESULTS: Twelve studies met the inclusion criteria. All included studies showed that DLT outcomes were comparable to outcomes of deceased donor liver transplantation (DDLT) in terms of mortality and complications. One-year patient survival rate ranged from 66.7% to 100%. Re-transplantation rate varied from 0 to 12.5%. Most frequent complications were related to biliary (3.7%-37.5%), hepatic artery (1.6%-9.1%), portal vein (12.5-33.3%) and hepatic vein events (1.6%), recurrence of domino donor disease (3.3%-17.4%) and graft rejection (16.7%-37.7%). The quality of the evidence was rated as moderate according to the Newcastle-Ottawa scale (NOS). CONCLUSION: DLT outcomes were similar to DDLT in terms of mortality and complications. Even though DLT will not solve the entire problem of organ shortage, transplant programs should always consider using this tool to maximize the availability of liver grafts.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".