Liver Transplantation for Hepatocellular Carcinoma. Working Group Report from the ILTS Transplant Oncology Consensus Conference
Bibliographic record
Abstract
Liver transplantation (LT) offers excellent long-term outcome for certain patients with hepatocellular carcinoma (HCC), with a push to not simply rely on tumor size and number. Selection criteria should also consider tumor biology (including alpha-fetoprotein), probability of waitlist and post-LT survival (ie, transplant benefit), organ availability, and waitlist composition. These criteria may be expanded for live donor LT (LDLT) compared to deceased donor LT though this should not adversely affect the double equipoise in LDLT, namely ensuring both acceptable recipient outcomes and donor safety. HCC patients with compensated liver disease and minimal tumor burden have low urgency for LT, especially after local-regional therapy with complete response, and do not appear to derive the same benefit from LT as other waitlist candidates. These guidelines were developed to assist in selecting appropriate HCC patients for both deceased donor LT and LDLT.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".