Deceased organ and tissue donation after medical assistance in dying: 2023 updated guidance for policy
Bibliographic record
Abstract
BACKGROUND: Since Canadian Blood Services (CBS) developed policy guidance in 2019 for organ and tissue donation after medical assistance in dying (MAiD), the federal government has made changes to legislation related to MAiD. This document provides updated guidance for clinicians, organ donation organizations, end-of-life care experts, MAiD providers and policy-makers on the impact of these changes. METHODS: Canadian Blood Services assembled a group of 63 experts from critical care, organ and tissue donation, health care administration, MAiD, bioethics, law and research to review the legislative changes in the Organ and Tissue Donation After Medical Assistance in Dying - Guidance for Policy forum. Two patients who had requested and been found eligible for MAiD and 2 family members of patients who had donated organs after MAiD were also included as participants. In a series of 3 online meetings from June 2021 to April 2022, forum participants addressed a variety of topics in small and large groups. These discussions were informed by a comprehensive scoping review using JBI methodology. We used an adapted form of nominal group technique to develop the recommendations, which were approved by consensus of the participants. Management of competing interests was in accordance with Guideline International Network principles. RECOMMENDATIONS: Although many of the recommendations from the guidance developed in 2019 are still relevant, this guidance provides 2 updated recommendations and 8 new recommendations in the following areas: referral to an organ donation organization, consent, directed and conditional donation, MAiD procedures, determination of death, health care professionals and reporting. INTERPRETATION: Policies and practices for organ and tissue donation after MAiD in Canada should align with current Canadian legislation. This updated guidance will help clinicians navigate the medical, legal and ethical challenges that arise when they support patients pursuing donation after MAiD.
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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 | 0.070 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.025 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".