Medical Assistance in Dying (MAiD) as a Source of Liver Grafts: Honouring the Ultimate Gift
Bibliographic record
Abstract
OBJECTIVE: To report the clinical outcomes of liver transplants from donors after medical assistance in dying (MAiD) versus donors after cardiac death (DCD) and deceased brain death (DBD). SUMMARY BACKGROUND DATA: In North America, the number of patients needing liver transplants exceeds the number of available donors. In 2016, MAiD was legalized in Canada. METHODS: All patients undergoing deceased donor liver transplantation at Toronto General Hospital between 2016 and 2021 were included in the study. Recipient perioperative and postoperative variables and donor physiological variables were compared among 3 groups. RESULTS: Eight hundred seven patients underwent deceased donor liver transplantation during the study period, including DBD (n=719; 89%), DCD (n=77; 9.5%), and MAiD (n=11; 1.4%). The overall incidence of biliary complications was 6.9% (n=56), the most common being strictures (n=55;6.8%), highest among the MAiD recipients [5.8% (DBD) vs. 14.2% (DCD) vs. 18.2% (MAiD); P =0.008]. There was no significant difference in 1 year (98.4% vs. 96.4% vs. 100%) and 3-year (89.3% vs. 88.7% vs. 100%) ( P =0.56) patient survival among the 3 groups. The 1- and 3- year graft survival rates were comparable (96.2% vs. 95.2% vs. 100% and 92.5% vs. 91% vs. 100%; P =0.37). CONCLUSION: With expected physiological hemodynamic challenges among MAiD and DCD compared with DBD donors, a higher rate of biliary complications was observed in MAiD donors, with no significant difference noted in short-and long-term graft outcomes among the 3 groups. While ethical challenges persist, good initial results suggest that MAiD donors can be safely used in liver transplantation, with results comparable with other established forms of donation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".