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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".