Sowing “seeds of trust”: How trust in normothermic regional perfusion is built in a continuum of care
Bibliographic record
Abstract
Normothermic regional perfusion (NRP) is a promising technology to improve organ transplantation outcomes by reversing ischemic injury caused by controlled donation after circulatory determination of death. However, it has not yet been implemented in Canada due to ethical questions. These issues must be resolved to preserve public trust in organ donation and transplantation. This qualitative, constructivist grounded theory study sought to understand how those most impacted by NRP perceived the ethical implications. We interviewed 29 participants across stakeholder groups of donor families, organ recipients, donation and transplantation system leaders, and care providers. The interview protocol included a short presentation about the purpose of NRP and procedures in abdomen versus chest and abdomen NRP, followed by questions probing potential violations of the dead donor rule and concerns regarding brain reperfusion. The results present a grounded theory placing NRP within a trust-building continuum of care for the donor, their family, and organ recipients. Stakeholders consistently described both forms of NRP as an ethical intervention, but their rationales were predicated on assumptions that neurologic criteria for death had been met following circulatory death determination. Empirical validation of these assumptions will help ground the implementation of NRP in a trust-preserving way.
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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 | 0.018 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".