Posttransplant Management of Recipients Undergoing Liver Transplantation for Hepatocellular Carcinoma. Working Group Report From the ILTS Transplant Oncology Consensus Conference
Bibliographic record
Abstract
Although liver transplantation (LT) is the best treatment for patients with localized hepatocellular carcinoma (HCC), recurrence occurs in 6%-18% of patients. Several factors, particularly morphological criteria combined with dynamic parameters, known before LT modify this risk and combined in prediction models may be used to stratify patients at need of variable surveillance strategies. Additional variables though likely explain differences in recurrence rates in patients with the same pre-LT HCC status. One of these variables is possibly immunosuppression (IS). Once recurrence takes place, management is highly heterogenous. Within the International Liver Transplantation Society Consensus Conference on Liver Transplant Oncology, working group 4 aim was to analyze the data regarding posttransplant management of recipients undergoing LT for HCC. Three areas of research were considered: (1) cancer prediction models and surveillance strategies; (2) tailored IS for cancer recipients; and (3) new adjuvant therapies for HCC recurrence. Following formulation of several questions, a literature search was undertaken with abstract review followed by article retrieval and full-data extraction. The grading of recommendations assessment, development and evaluation (GRADE) system was used for evidence rating incorporating strength of recommendation and quality of evidence.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".