Ethical and logistical concerns for establishing NRP-cDCD heart transplantation in the United States
Bibliographic record
Abstract
Controlled heart donation after circulatory determination of death (cDCD) is well established internationally with good outcomes and could be adopted in the United States to increase heart supply if ethical and logistical challenges are comprehensively addressed. The most effective and resource-efficient method for mitigating warm ischemia after circulatory arrest is normothermic regional perfusion (NRP) in situ. This strategy requires restarting circulation after declaration of death according to circulatory criteria, which appears to challenge the legal circulatory death definition requiring irreversible cessation. Permanent cessation for life-saving efforts must be achieved to assuage this concern and ligating principal vessels maintains no blood flow to the brain, which ensures natural progression to cessation of brain function. This practice-standard in some countries-raises unique concerns about prioritizing life-saving efforts, informed authorization from decision-makers, and the clinician's role in the patient's death. To preserve public trust, medical integrity, and respect for the donor, the donation conversation must not take place until after an un-coerced decision to withdraw life-sustaining treatment made in accordance with the patient's treatment goals. The decision-maker(s) must understand cDCD procedure well enough to provide genuine authorization and the preservation/procurement teams must be kept separate from the clinical care team.
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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 | 0.064 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".