European Society for Organ Transplantation (ESOT) Consensus Statement on the Role of Pancreas Machine Perfusion to Increase the Donor Pool for Beta Cell Replacement Therapy
Bibliographic record
Abstract
The advent of Machine Perfusion (MP) as a superior form of preservation and assessment for cold storage of both high-risk kidney's and the liver presents opportunities in the field of beta-cell replacement. It is yet unknown whether such techniques, when applied to the pancreas, can increase the pool of suitable donor organs as well as ameliorating the effects of ischemia incurred during the retrieval process. Recent experimental models of pancreatic MP appear promising. Applications of MP to the pancreas, needs refinement regarding perfusion protocols and organ viability assessment criteria. To address the "Role of pancreas machine perfusion to increase the donor pool for beta cell replacement," the European Society for Organ Transplantation (ESOT) assembled a dedicated working group comprising of experts to review literature pertaining to the role of MP as a method of improving donor pancreas quality as well as quantity available for transplant, and to develop guidelines founded on evidence-based reviews in experimental and clinical settings. These were subsequently refined during the Consensus Conference when this took place in Prague.
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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 | 0.052 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".