New methods for improving pancreas preservation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pancreas and islet transplantation face critical organ shortage challenges, with many potential grafts discarded due to concerns about consequences of ischemia-reperfusion injury, particularly from donation after circulatory death (DCD) donors. Static cold storage remains standard practice but has significant limitations. Novel preservation technologies may improve transplant outcomes, donor selection and even expand the donor pool. RECENT FINDINGS: Normothermic regional perfusion in DCD donors has increased pancreas utilization with promising one-year graft survival comparable to donation after brain-death (DBD) donors. Hypothermic machine perfusion maintains tissue integrity and shows promising preclinical results. Oxygenated hypothermic machine perfusion successfully restores tissue adenosine triphosphate (ATP) levels without notable tissue injury. Normothermic machine perfusion, despite challenges, offers potential for viability assessment and resuscitation. SUMMARY: Advanced preservation technologies provide platforms for assessment, reconditioning, and therapeutic interventions for pancreas grafts. Clinical translation requires consensus on perfusion parameters and perfusate composition optimized for pancreatic preservation. Future developments should focus on implementing sensitive and specific assessment methods, including beta-cell specific biomarkers, to confidently select and utilize marginal pancreas grafts for transplantation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".