Liver Transplantation for Hepatocellular Carcinoma: An Expanding Cornerstone of Care in the Era of Immunotherapy
Bibliographic record
Abstract
Liver transplantation (LT) has been accepted as a cornerstone of care in hepatocellular carcinoma (HCC) for almost three decades. In recent years, its role has been evolving to include patients with disease burden beyond the widely used Milan criteria. The integration of dynamic biomarkers such as alpha-fetoprotein together with downstaging approaches and tumor evolution after enlistment has allowed the selection of patients most likely to benefit, resulting in 5-year survival rates greater that 70%. With the increasing use of immune checkpoint inhibitors (ICIs) across all stages of disease, alone or in combination with locoregional therapies, there is now the potential to further expand the patient population with HCC who may benefit from LT. This brings challenges, given the global shortage of organs and the need to better understand the optimal use of ICIs before transplantation. Furthermore, the field of transplant oncology awaits additional biomarkers that can predict those likely to benefit from ICIs. More than ever, a multidisciplinary approach for liver cancer management is critical to ensure all patients are considered for LT where appropriate, and do not miss the opportunity for long-term survival.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.001 |
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".