Recent developments and ongoing trials in transplant oncology
Bibliographic record
Abstract
Over the past two decades since the introduction of the Milan criteria, the field of transplant oncology has undergone a rapid development with a rising proportion of liver transplantations being performed for oncological indications. For many patients with liver tumours, transplantation represents the only chance for cure. However, many challenges remain, such as the adequate patient selection, management of post-transplant recurrence and refinement of neoadjuvant treatment protocols. This review provides an overview of the current state of the art of liver transplantation for oncological indications such as hepatocellular carcinoma, cholangiocarcinoma, colorectal liver metastasis and metastatic neuroendocrine tumours. We also summarize the ongoing research and explore future trends. Clinical trials are currently studying new diagnostic modalities, innovative pharmacological treatments, novel surgical techniques, downstaging regimens and new indications for liver transplantation. These emerging results will continue to shape the field of transplant oncology and provide us with the necessary tools to better select, treat and follow patients with liver tumours qualifying for liver transplantation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".