The management of post-transplantation recurrence of hepatocellular carcinoma
Bibliographic record
Abstract
The annual incidence of hepatocellular carcinoma (HCC) continues to rise. Over the last two decades, liver transplantation (LT) has become the preferable treatment of HCC, when feasible and strict selection criteria are met. With the rise in HCC-related LT, compounded by downstaging techniques and expansion of transplant selection criteria, a parallel increase in number of post-transplantation HCC recurrence is expected. Additionally, in the context of an immunosuppressed transplant host, recurrences may behave aggressively and more challenging to manage, resulting in poor prognosis. Despite this, no consensus or best practice guidelines for post-transplantation cancer surveillance and recurrence management for HCC currently exist. Studies with adequate population sizes and high-level evidence are lacking, and the role of systemic and locoregional therapies for graft and extrahepatic recurrences remains under debate. This review seeks to summarize the existing literature on post-transplant HCC surveillance and recurrence management. It highlights the value of early tumour detection, re-evaluating the immunosuppression regimen, and staging to differentiate disseminated recurrence from intrahepatic or extrahepatic oligo-recurrence. This ultimately guides decision-making and maximizes treatment effect. Treatment recommendations specific to recurrence type are provided based on currently available locoregional and systemic therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".