Liver transplantation for intrahepatic cholangiocarcinoma: who, when and how
Bibliographic record
Abstract
PURPOSE OF REVIEW: Using transplant oncology principles, selected patients with intrahepatic cholangiocarcinoma (iCCA) may achieve long-term survival after liver transplantation. Strategies for identifying and managing these patients are discussed in this review. RECENT FINDINGS: Unlike initial reports, several modern series have reported positive outcomes after liver transplantation for iCCA. The main challenges are in identifying the appropriate candidates and graft scarcity. Tumor burden and response to neoadjuvant therapies have been successfully used to identify favorable biology in unresectable cases. New molecular biomarkers will probably predict this response in the future. Also, new technologies and better strategies have been used to increase graft availability for these patients without affecting the liver waitlist. SUMMARY: Liver transplantation for the management of patients with unresectable iCCA is currently a reality under strict research protocols. Who is a candidate for transplantation, when to use neoadjuvant and locoregional therapies, and how to increase graft availability are the main topics of this review.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.001 |
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".