Living Donor Liver Transplantation for Hepatocellular Carcinoma Within and Outside Traditional Selection Criteria
Bibliographic record
Abstract
OBJECTIVE: To evaluate long-term oncologic outcomes of patients post-living donor liver transplantation (LDLT) within and outside standard transplantation selection criteria and the added value of the incorporation of the New York-California (NYCA) score. BACKGROUND: LDLT offers an opportunity to decrease the liver transplantation waitlist, reduce waitlist mortality, and expand selection criteria for patients with hepatocellular carcinoma (HCC). METHODS: Primary adult LDLT recipients between October 1999 and August 2019 were identified from a multicenter cohort of 12 North American centers. Posttransplantation and recurrence-free survival were evaluated using the Kaplan-Meier method. RESULTS: Three hundred sixty LDLTs were identified. Patients within Milan criteria (MC) at transplantation had a 1, 5, and 10-year posttransplantation survival of 90.9%, 78.5%, and 64.1% versus outside MC 90.4%, 68.6%, and 57.7% ( P = 0.20), respectively. For patients within the University of California San Francisco (UCSF) criteria, respective posttransplantation survival was 90.6%, 77.8%, and 65.0%, versus outside UCSF 92.1%, 63.8%, and 45.8% ( P = 0.08). Fifty-three (83%) patients classified as outside MC at transplantation would have been classified as either low or acceptable risk with the NYCA score. These patients had a 5-year overall survival of 72.2%. Similarly, 28(80%) patients classified as outside UCSF at transplantation would have been classified as a low or acceptable risk with a 5-year overall survival of 65.3%. CONCLUSIONS: Long-term survival is excellent for patients with HCC undergoing LDLT within and outside selection criteria, exceeding the minimum recommended 5-year rate of 60% proposed by consensus guidelines. The NYCA categorization offers insight into identifying a substantial proportion of patients with HCC outside the MC and the UCSF criteria who still achieve similar post-LDLT outcomes as patients within the criteria.
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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 | 0.001 |
| 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) | 0.001 | 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".