Large-Animal Model of Donation after Circulatory Death and Normothermic Regional Perfusion for Cardiac Assessment
Bibliographic record
Abstract
The increase in demand for cardiac transplantation throughout the years has fueled interest in donation after circulatory death (DCD) to expand the organ donor pool. However, the DCD process is associated with the risk of cardiac tissue injury due to the inevitable period of warm ischemia. Normothermic regional perfusion (NRP) allows for an in situ organ assessment, allowing the procurement of hearts determined to be viable. Here, we describe a clinically relevant large-animal model of DCD followed by NRP. Circulatory death is established in anesthetized pigs by stopping mechanical ventilation. After a preset warm ischemia period, an extracorporeal membrane oxygenator (ECMO) is used for a NRP period lasting at least 30 min. During this reperfusion period, the model allows the collection of various myocardial biopsies and blood samples for initial cardiac evaluation. Once NRP is weaned, biochemical, hemodynamic, and echocardiographic assessments of cardiac function and metabolism can be performed before organ procurement. This protocol closely simulates the clinical scenario previously described for DCD and NRP in heart transplantation and has the potential to facilitate studies aimed at decreasing ischemia-reperfusion injury and enhance cardiac functional preservation and recovery.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".