Festina lente—to advance we need to make haste slowly. Living donor liver transplant for colorectal cancer liver metastases
Bibliographic record
Abstract
It is becoming increasingly clear that the indication for liver transplant (LT) for unresectable colorectal liver metastases (CRLM) has come to stay. There is a growing body of evidence demonstrating the benefit for a highly selected group of patients, and this manuscript is no exception. Kaltenmeier et al 1 Kaltenmeier C. Geller D.A. Ganesh S. et al. Living Donor Liver Transplantation for Colorectal Cancer Liver Metastases (CRLM): midterm outcomes at a single center in North America. Am J Transplant. 2023; (S1600-6135(23)00689-5, Published online October 5)https://doi.org/10.1016/j.ajt.2023.09.001 Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar reported the outcomes of a single-center series of 10 patients undergoing living donor LT (LDLT) for CRLM. We carefully reviewed the manuscript and would like to congratulate the authors for further contributing to the current literature (Table 1), especially since their institution is one of the most experienced in LDLT in the US. Several comments emerge from it that we would like to highlight, since this is an indication that, although it is already well established as a therapeutic strategy, it continues to raise controversy as some concepts have yet to be standardized. Table 1Published series of patients treated with liver transplant for colorectal cancer liver metastases. Author Center Year Number of patients Overall survival Hagness et al 3 Hagness M Foss A Line PD et al. Liver transplantation for nonresectable liver metastases from colorectal cancer. Ann Surg. 2013; 257: 800-806https://doi.org/10.1097/SLA.0b013e31828239574 Crossref PubMed Scopus (0) Google Scholar Oslo University Hospital 2013 21 60% at 5 y Dueland et al 4 Dueland S Syversveen T Solheim JM et al. Survival following liver transplantation for patients with nonresectable liver-only colorectal metastases. Ann Surg. 2020; 271: 212-218https://doi.org/10.1097/SLA.0000000000003404 Crossref PubMed Scopus (176) Google Scholar Oslo University Hospital 2020 15 83% at 5 y Hernandez-Alejandro et al 5 Hernandez-Alejandro R. Ruffolo L.I. Sasaki K. et al. Recipient and donor outcomes after living-donor liver transplant for unresectable colorectal liver metastases. JAMA Surg. 2022; 157: 524-530https://doi.org/10.1001/jamasurg.2022.0300 Crossref PubMed Scopus (39) Google Scholar URMC, CCF, and Toronto General Hospital 2022 10 100% at 1.5 y Rajendran et al 6 Rajendran L Claasen MP McGilvray ID et al. Toronto management of initially unresectable liver metastasis from colorectal cancer in a living donor liver transplant program. J Am Coll Surg. 2023; 237: 231-242https://doi.org/10.1097/XCS.0000000000000734 Crossref PubMed Scopus (11) Google Scholar Toronto General Hospital 2023 7 100% at 3 y Abbreviations: CCF, Cleveland Clinic Foundation; URMC, University of Rochester Medical Center. Open table in a new tab Abbreviations: CCF, Cleveland Clinic Foundation; URMC, University of Rochester Medical Center.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.013 | 0.029 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".