Utilization of liver grafts obtained from donation after medical assistance in dying: A Canadian multicenter experience
Bibliographic record
Abstract
BACKGROUND & AIMS: Given country-specific variance in legislation, limited information is available regarding outcomes of liver transplantation (LT) using medical assistance in dying (MAiD) donation after circulatory death (DCD type-V) grafts. We compared outcomes following LT using DCD type-V vs. conventional DCD type-III grafts. METHODS: We reviewed all LTs using DCD-V and DCD-III donors performed across Canadian provinces between 2016 (MAiD legalization) and 2023. Primary outcomes were patient and graft survival at 1, 3, and 5 years. Secondary outcomes included early allograft dysfunction, length of stay, postoperative complications, and biliary complications. RESULTS: Among 313 recipients, 56 (17.9%) received DCD-V and 257 (82.1%) DCD-III grafts. DCD-V donors were older (56 vs. 38 years, p <0.0001), and had similar warm ischemic time (20 vs. 23 min, p = 0.190). Patient survival for DCD-V was 89.3%, 85.7%, and 85.7% at 1, 3, and 5 years, with graft survival of 82.1%, 78.6%, and 78.6%, comparable to DCD-III. DCD-V was not associated with graft loss (odds ratio 1.64; 95% CI 0.66-4.08), which was confirmed by multivariable Cox regression analysis (hazard ratio 1.53; 95% CI 0.74-3.15). Early allograft dysfunction occurred more frequently with DCD-III (57.5% vs. 42.9%, p = 0.047), whereas length of stay was longer with DCD-V (21.5 vs. 15 days, p = 0.011). Overall complication rates were similar; biliary complications were more common in DCD-V, but this was not statistically significant (32.1% vs. 23.1%, p = 0.153). CONCLUSIONS: Liver grafts from MAiD donors provide outcomes comparable to DCD-III and increased Canadian DCD activity by 21.8%. These findings support MAiD as a valuable additional source of liver grafts, though larger studies are needed to validate long-term safety and efficacy. IMPACT AND IMPLICATIONS: Liver transplantation using grafts from donors following medical assistance in dying (MAiD) achieves satisfactory patient and graft survival, with complication rates comparable to those following conventional donation after circularly death. MAiD donation increased donation after circularly death liver transplant activity in Canada by 21.8% over 7 years. These findings support MAiD as a valuable additional source of grafts, while also honoring the wishes of patients who choose to donate their organs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".