De Novo Malignancy After Liver Transplantation: Risk Assessment, Prevention, and Management—Guidelines From the ILTS-SETH Consensus Conference
Bibliographic record
Abstract
De novo malignancies (DNMs) following liver transplantation (LT) have been reported as 1 of the major causes of late mortality, being the most common cause of death in the second decade after LT. The overall incidence of DNMs is reported to be in the range of 3.1% to 14.4%, and the incidence is 2- to 3-fold higher in transplant recipients than in age- and sex-matched healthy controls. Long-term immunosuppressive therapy, which is the key in maintaining host tolerance and achieving good long-term outcomes, is known to contribute to a higher risk of DNMs. However, the incidence and type of DNM also depends on different risk factors, including patient demographics, cause of the underlying chronic liver disease, behavior (smoking and alcohol abuse), and pre-existing premalignant conditions. The estimated standardized incidence ratio for different DNMs is also variable. The International Liver Transplantation Society-Spanish Society of Liver Transplantation Consensus Conference working group on DNM has summarized and discussed the current available literature on epidemiology, risk factors, management, and survival after DNMs. Recommendations for screening and surveillance for specific tumors, as well as immunosuppression and cancer-specific management in patients with DNM, are summarized.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".