Normothermic Machine Perfusion of Discarded Liver Grafts—What Is Viable?
Bibliographic record
Abstract
To the Editor: We read the paper by op den Dries et al. 1 analyzing Ex Vivo Normothermic Machine Perfusion and Viability Testing of Discarded Human Donor Livers with great interest. This paper is noteworthy for its careful analysis and honest reporting. We congratulate the authors for (a) the outstanding technical approach to preserve human livers during normothermic ex vivo liver perfusion; (b) highlighting the importance of using marginal grafts to expand the donor pool; and (c) protection of the graft during ex vivo liver perfusion. The human ex vivo liver perfusion model of op den Dries et al. is a promising approach to improve marginal grafts and to better assess livers prior to transplantation. J. M. Knaak1, V. N. Spetzler1, N. Selzner2 and M. Selzner1,* 1Department of Surgery, Multi Organ Transplant Program, Toronto General Hospital, Toronto, Canada 2Department of Medicine, Multi Organ Transplant Program, Toronto General Hospital, Toronto, Canada * Corresponding author: Markus Selzner, markus.selzner@uhn.ca The authors of this letter have no conflicts of interest to disclose as described by the American Journal of Transplantation.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".