Kidney transplant outcomes after medical assistance in dying
Bibliographic record
Abstract
INTRODUCTION: After nearly four years of Canadian experience with medical assistance in dying (MAID), the clinical volume of organ transplantation following MAID remains low. This is the first Canadian report evaluating recipient outcomes from kidney transplantation following MAID. METHODS: This was a retrospective review of the first nine cases of kidney transplants following MAID at a Canadian transplant center. RESULTS: Nine patients underwent MAID followed by kidney retrieval during the study period. Their diagnoses were largely neuromuscular diseases. The mean warm ischemic time was 20 minutes (standard deviation [SD] 7). The nine recipients had a mean age of 60 (SD 19.7). The mean cold ischemic time was 525 minutes (SD 126). Delayed graft function occurred in only one patient out of nine. The mean 30-day creatinine was 124 umol/L (SD 52). The mean three-month creatinine was 115 umol/L (SD 29). CONCLUSIONS: We report nine cases of kidney transplantation following MAID. The process minimized warm ischemia, resulting in low delayed graft function rates, and acceptable post-transplant outcomes. Further large-scale research is necessary to optimize processes and outcomes in this novel clinical pathway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".