Practice and challenges for organ donation after medical assistance in dying: A scoping review including the results of the first international roundtable in 2021
Bibliographic record
Abstract
The procedure combining medical assistance in dying (MAiD) with donations after circulatory determination of death (DCDD) is known as organ donation after euthanasia (ODE). The first international roundtable on ODE was held during the 2021 WONCA family medicine conference as part of a scoping review. It aimed to document practice and related issues to advise patients, professionals, and policymakers, aiding the development of responsible guidelines and helping to navigate the issues. This was achieved through literature searches and national and international stakeholder meetings. Up to 2021, ODE was performed 286 times in Canada, the Netherlands, Spain, and Belgium, including eight cases of ODE from home (ODEH). MAiD was provided 17,217 times (2020) in the eight countries where ODE is permitted. As of 2021, 837 patients (up to 14% of recipients of DCDD donors) had received organs from ODE. ODE raises some important ethical concerns involving patient autonomy, the link between the request for MAiD and the request to donate organs and the increased burden placed on seriously ill MAiD patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".