Heart donation and transplantation after circulatory determination of death: expert guidance from a Canadian consensus building process
Bibliographic record
Abstract
Controlled donation after circulatory determination of death (DCD), where death is determined after cardiac arrest, has been responsible for the largest quantitative increase in Canadian organ donation and transplants, but not for heart transplants. Innovative international advances in DCD heart transplantation include direct procurement and perfusion (DPP) and normothermic regional perfusion (NRP). After death is determined, DPP involves removal and reanimation of the arrested heart on an ex situ organ perfusion system. Normothermic regional perfusion involves surgically interrupting (ligating the aortic arch vessels) brain blood flow after death determination, followed by restarting the heart and circulation in situ using extracorporeal membrane oxygenation. The objectives of this Canadian consensus building process by a multidisciplinary group of Canadian stakeholders were to review current evidence and international DCD heart experience, comparatively evaluate international protocols with existing Canadian medical, legal, and ethical practices, and to discuss implementation barriers. Review of current evidence and international experience of DCD heart donation (DPP and NRP) determined that DCD heart donation could be used to provide opportunities for more heart transplants in Canada, saving additional lives. Although candid discussion identified a number of potential barriers and challenges for implementing DCD heart donation in Canada, it was determined that DPP implementation is feasible (pending regulatory approval for the use of an ex situ perfusion device in humans) and in alignment with current medical guidelines for DCD. Nevertheless, further work is required to evaluate the consistency of NRP with current Canadian death determination policy and to ensure the absence of brain perfusion during this process.
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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 | 0.251 | 0.237 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".