Nonhepatic Cancer in the Pediatric Liver Transplant Population: Guidelines From the ILTS-SETH Consensus Conference
Bibliographic record
Abstract
The incidence and geographical distribution of cancers in children are dramatically different from the adult population. Consequent to improvements in postcancer survival, there is a progressive increase in the number of patients requiring liver transplantation (LT) who are in remission from pretransplant malignancy (PTM). Conventionally, however, PTM has been considered a relative contraindication to LT. Furthermore, with improving post-LT survival now extending beyond decades, the cumulative effect of immunosuppression and the increasing risk of de novo cancers need to be acknowledged. A working group was formed to evaluate, discuss, and retrieve all the evidence and provide guidelines with regards to best practices surrounding nonhepatic cancer in the pediatric LT (PLT) population. Further subsections of research included (a) extrahepatic solid tumors, leukemia, lymphoma, and other hematological disturbances before PLT and (b) malignancies following PLT (including posttransplant lymphoproliferative disorders). This guidance provides a collection of evidence-based expert opinions, consensus, and best practices on nonhepatic cancers in PLT.
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".