Transplantation of Hearts Donated after Circulatory Death
Bibliographic record
Abstract
Cardiac transplantation has become limited by a critical shortage of suitable organs from brain-dead donors. Reports describing the successful clinical transplantation of hearts donated following circulatory death (DCD) have recently emerged. Hearts from DCD donors suffer significant ischemic injury prior to organ procurement; therefore, the traditional approach to the transplantation of hearts from brain-dead donors is not applicable to the DCD context. Advances in our understanding of ischemic post-conditioning have facilitated the development of DCD heart resuscitation strategies that can be used to minimize ischemia-reperfusion injury at the time of organ procurement. The availability of a clinically approved ex situ heart perfusion device now allows DCD heart preservation in a normothermic beating state and minimizes exposure to incremental cold ischemia. This technology also facilitates assessments of organ viability to be undertaken prior to transplantation, thereby minimizing the risk of primary graft dysfunction. The application of a tailored approach to DCD heart transplantation that focuses on organ resuscitation at the time of procurement, ex situ preservation, and pre-transplant assessments of organ viability has facilitated the successful clinical application of DCD heart transplantation. The transplantation of hearts from DCD donors is now a clinical reality. Investigating ways to optimize the resuscitation, preservation, evaluation, and long-term outcomes is vital to ensure a broader application of DCD heart transplantation in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".