Novel Benchmark for Adult-to-Adult Living-donor Liver Transplantation
Bibliographic record
Abstract
OBJECTIVE: To define benchmark values for adult-to-adult living-donor liver transplantation (LDLT). BACKGROUND: LDLT utilizes living-donor hemiliver grafts to expand the donor pool and reduce waitlist mortality. Although references have been established for donor hepatectomy, no such information exists for recipients to enable conclusive quality and comparative assessments. METHODS: Patients undergoing LDLT were analyzed in 15 high-volume centers (≥10 cases/year) from 3 continents over 5 years (2016-2020), with a minimum follow-up of 1 year. Benchmark criteria included a Model for End-stage Liver Disease ≤20, no portal vein thrombosis, no previous major abdominal surgery, no renal replacement therapy, no acute liver failure, and no intensive care unit admission. Benchmark cutoffs were derived from the 75th percentile of all centers' medians. RESULTS: Of 3636 patients, 1864 (51%) qualified as benchmark cases. Benchmark cutoffs, including posttransplant dialysis (≤4%), primary nonfunction (≤0.9%), nonanastomotic strictures (≤0.2%), graft loss (≤7.7%), and redo-liver transplantation (LT) (≤3.6%), at 1-year were below the deceased donor LT benchmarks. Bile leak (≤12.4%), hepatic artery thrombosis (≤5.1%), and Comprehensive Complication Index (CCI ® ) (≤56) were above the deceased donor LT benchmarks, whereas mortality (≤9.1%) was comparable. The right hemiliver graft, compared with the left, was associated with a lower CCI ® score (34 vs 21, P < 0.001). Preservation of the middle hepatic vein with the right hemiliver graft had no impact neither on the recipient nor on the donor outcome. Asian centers outperformed other centers with CCI ® score (21 vs 47, P < 0.001), graft loss (3.0% vs 6.5%, P = 0.002), and redo-LT rates (1.0% vs 2.5%, P = 0.029). In contrast, non-benchmark low-volume centers displayed inferior outcomes, such as bile leak (15.2%), hepatic artery thrombosis (15.2%), or redo-LT (6.5%). CONCLUSIONS: Benchmark LDLT offers a valuable alternative to reduce waitlist mortality. Exchange of expertise, public awareness, and centralization policy are, however, mandatory to achieve benchmark outcomes worldwide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".