Outcomes from organ donation following medical assistance in dying:A sset.' scoping review
Bibliographic record
Abstract
AIM To Compare and sumniaize the cLnrie s Latrnaionalliteiature o the transpla trcipient ocomes oforgan gon Medical Assistane in Dyig (MAi) dnors, asvefl as e actual and)otentiailmpact of oiran ionation followint MAID n the donation and transplantation system.Background: The provision of organ donation following MAiD can impact the donation and transplantation system, as well as potential recipients oforgans froiniie MAD donor. therefore a compiehensive undeistanding of the potential and actUal impactof organ donation after MAD on the donation and transplantation systems is needed.Design:Scoping review using the JBI fianlewoik.Methods. We searched for published (MEDLINE, Enibase, CINAHL, PsycINFO, Web of Science, and Academic Search Complete, and unpublished literature (organ donation organization weasites worldwide). Included teferences discussed the actual and potential inipact of organ donation following MAiD on the donation and transplantation systen All references were screened, extracted and analysed by Two independent reviewers.Resuits: We included 78 references in this review andou finding weresuninarized across three categories: (1) Impact in the donor pool: (2) statistics on organ donation following MAiD. and (3) potential and actual impact of MAD on the donation and transplant system.Conclusions:The potential impact of the MAiD donor on the transplant waiting list is relatively small as this process is still rare, however, due to the culrrent organ shortageworldwide the contribution of this procedure should not be disregarded. Additionally, despite being limited, the existing research provided scanty evidence that organs retrieved fion MAID donos ae associated with satisfactory giaft function and snuvival rates and that outcones fLon nansplant recipients are comparable to those of organs fros donation following brain death and msay hebetter than those of organs fion other types of donation after circulatory deteinined death. Still, fuithe studies are required for comprehensive and reliable evidence.
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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 | 0.012 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".