The New Era of Transplant Oncology: Liver Transplantation for Nonresectable Colorectal Cancer Liver Metastases
Bibliographic record
Abstract
Colorectal cancer (CRC) is the third most incident cancer worldwide. Most of CRC patients will develop distant metastases, mainly to the liver, and liver resection is the only potential chance for cure. On the other hand, only a small proportion of patients with hepatic CRC metastasis are candidates for upfront liver resection. Liver transplantation (LT) is an attractive option for patients with nonresectable CRC liver metastases (NRCLM) without extrahepatic involvement. Initial experiences with LT for NRCLM achieved very poor outcomes, with a 5-year overall survival (OS) lower than 20%. However, these initial studies did not have a standardized patient selection or neoadjuvant or adjuvant therapies. With recent advances in the surgical and medical oncology fields, the landscape has changed. Recent studies from Norway have shown an encouraging 5-year OS of 50% when transplanting patients with NRCLM. Nevertheless, the main concern when expanding the indications for LT is organ shortage. To manage this organ shortage, strategies utilizing live donor liver transplantation are gaining favor. A few ongoing trials are assessing the impact of LT in NRCLM patient survival. Therefore, the aim of this paper is to review the current status of LT for NRCLM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".