Outcomes of lung transplantation from organ donation after medical assistance in dying: First North American experience
Bibliographic record
Abstract
Over 2.5% of deaths in Canada occur as a result from medical assisting in dying (MAID), and a subset of these deaths result in organ donation. However, detailed outcomes of lung transplant recipients using these donors is lacking. This is a retrospective single center cohort study comparing lung transplantation outcomes after donation using MAID donors compared to neurologically determined death and controlled donation after circulatory death (NDD/cDCD) donors from February 2018 to July 2021. Thirty-three patients received lungs from MAID donors, and 560 patients received lungs from NDD/cDCD donors. The donor diagnoses leading to MAID provision were degenerative neurological diseases (n = 33) and end stage organ failure (n = 5). MAID donors were significantly older than NDD/cDCD donors (56 [IQR 49-64] years vs. 48 [32-59]; p = .0009). Median ventilation period and 30 day mortality were not significantly different between MAID and NDD/cDCD lungs recipients (ventilation: 1 day [1-3] vs 2 days [1-3]; p = .37, deaths 0% [0/33] vs. 2% [11/560], p = .99 respectively). Intermediate-term outcomes were also similar. In summary, for lung transplantation using donors after MAID, recipient outcomes were excellent. Therefore, where this practice is permitted, donation after MAID should be strongly considered for lung transplantation as a way to respect donor wishes while substantially improving outcomes for recipients with end-stage lung disease.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".