Liver transplantation with donation after medical assistance in dying: Case series and systematic review of the literature
Bibliographic record
Abstract
Medical assistance in dying (MAiD) has been a legally approved practice in Canada since 2016. Only recently have patients undergoing MAiD also been considered as donors for liver transplantation (LT). This study aimed to evaluate a case series of LT outcomes for recipients with MAiD donors and was paired with a systematic literature review of studies assessing the efficacy of MAiD-associated liver donation. A retrospective chart review of patients registered within the LT Registry at London Health Sciences Centre (LHSC) in London, Ontario, Canada, that had received MAiD donor LT was conducted to develop a case series. Descriptive statistics were produced based on available patient outcomes information. The systematic review included euthanasia due to MAiD being a term exclusive to Canada. Case series had a 100% 1-year graft survival rate, with 50% of patients experiencing early allograft dysfunction but having no significant clinical outcome. A single case of postoperative biliary complication was reported. Median warm ischemic time ranged from 7.8-13 minutes among case series and literature reviews. Utilization of donation after circulatory death allografts procured after MAiD appears to be promising. Mechanisms associated with potential impact in postoperative outcomes include relatively lower warm ischemic time relative to donation after circulatory death Maastricht III graft recipients.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".