The COVID-19 pandemic and organ donation and transplantation: ethical issues
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has had a significant impact on the health system worldwide. The organ and tissue donation and transplantation (OTDT) system is no exception and has had to face ethical challenges related to the pandemic, such as risks of infection and resource allocation. In this setting, many Canadian transplant programs halted their activities during the first wave of the pandemic. METHOD: To inform future ethical guidelines related to the COVID-19 pandemic or other public health emergencies of international concern, we conducted a literature review to summarize the ethical issues. RESULTS: This literature review identified three categories of ethical challenges. The first one describes the general ethical issues and challenges reported by OTDT organizations and transplantation programs, such as risks of COVID-19 transmission and infection to transplant recipients and healthcare professionals during the transplant process, risk of patient waitlist mortality or further resource strain where transplant procedures have been delayed or halted, and resource allocation. The second category describes ethical challenges related to informed consent in the context of uncertainty and virtual consent. Finally, the third category describes ethical issues related to organ allocation, such as social considerations in selecting transplant candidates. CONCLUSION: This literature review highlights the salient ethical issues related to OTDT during the current COVID-19 pandemic. As medical and scientific knowledge about COVID-19 increases, the uncertainties related to this disease will decrease and the associated ethical issues will continue to evolve.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".