Machine Perfusion of Donor Livers for Transplantation: A Proposal for Standardized Nomenclature and Reporting Guidelines
Bibliographic record
Abstract
With increasing demand for donor organs for transplantation, machine perfusion (MP) promises to be a beneficial alternative preservation method for donor livers, particularly those considered to be of suboptimal quality, also known as extended criteria donor livers. Over the last decade, numerous studies researching MP of donor livers have been published and incredible advances have been made in both experimental and clinical research in this area. With numerous research groups working on MP, various techniques are being explored, often applying different nomenclature. The objective of this review is to catalog the differences observed in the nomenclature used in the current literature to denote various MP techniques and the manner in which methodology is reported. From this analysis, we propose a standardization of nomenclature on liver MP to maximize consistency and to enable reliable comparison and meta-analyses of studies. In addition, we propose a standardized set of guidelines for reporting the methodology of future studies on liver MP that will facilitate comparison as well as clinical implementation of liver MP procedures.
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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 | 0.066 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.001 | 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".