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Record W4384925677 · doi:10.11124/jbies-22-00143

Organ donation following medical assistance in dying, Part I: a scoping review of legal and ethical aspects

2023· review· en· W4384925677 on OpenAlexafffundabout
Vanessa Silva e Silva, Amina Silva, Andrea Rochon, Ken Lotherington, Laura Hornby, Tineke Wind, Jan Bollen, Lindsay Wilson, Aimee Sarti, Sonny Dhanani

Bibliographic record

VenueJBI Evidence Synthesis · 2023
Typereview
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsUniversity of OttawaCanadian Blood ServicesSt. Lawrence CollegeBrock UniversityOttawa HospitalChildren's Hospital of Eastern Ontario
FundersHealth CanadaCanadian Blood ServicesAustralian Government
KeywordsOrgan donationEngineering ethicsDonationMedicinePsychologyPolitical scienceLawEngineeringTransplantationSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this review was to collate and summarize the current literature on what is known about organ donation following medical assistance in dying (MAiD). Additionally, for this first part of a 2-part scoping review, the focus is on legal and ethical considerations regarding organ donation following MAiD. INTRODUCTION: Organ donation following MAiD is a relatively new procedure that has sparked much debate and discussion. A comprehensive investigation into the legal and ethical aspects related to organ donation following MAiD is needed to inform the development of safe and ethical practices. INCLUSION CRITERIA: In this review, we included documents that investigated legal and/or ethical issues related to individuals who underwent organ donation following MAiD in any setting (eg, hospital or home) worldwide. We considered quantitative and qualitative studies, text and opinion papers, gray literature, and unpublished material provided by stakeholders. METHODS: This scoping review followed JBI methodology. Published studies were retrieved from databases, including MEDLINE (Ovid), Embase (Ovid), CINAHL (EBSCOhost), PsycINFO (Ovid), Web of Science Core Collection, and Academic Search Complete (EBSCOhost). Gray and unpublished literature included reports from organ donation organizations in Canada, The Netherlands, and Belgium. Two independent reviewers screened all reports (both by title and abstract and by full text) against the inclusion criteria, extracted data, and completed a content analysis. Disagreements between the 2 reviewers were resolved through discussions among the reviewers and the lead reviewer. RESULTS: We included 121 documents for parts I and II of our scoping review, 89 of which are included in part I. The majority of the 89 documents were discussion papers published in English and in Canada from 2019 to 2021. In the content analysis, we identified 4 major categories regarding ethical and legal aspects of organ donation following MAiD: i) legal definitions, legislation, and guidelines; ii) ethics, dilemmas, and consensus; iii) consent and objection; and iv) public perceptions. We identified the main legislation regulating the practices of organ donation following MAiD in countries where both procedures are permitted, the many ethical debates surrounding this topic (eg, eligibility criteria for organ donation and MAiD, disclosure of donors' and recipients' information, directed organ donation, death determination in organ donation following MAiD, ethical safeguards for organ donation following MAiD), as well as the public perceptions of this process. CONCLUSIONS: Organ donation following MAiD has raised many legal and ethical concerns regarding establishing safeguards to protect patients and families. Despite the ongoing debates around the risks and benefits of this combined procedure, when patients who request MAiD want to donate their organs, this option can help fulfill their last wishes and diminish their suffering, which should be the main reasons to offer organ donation following MAiD.

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 imitation

Not 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.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.152
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.039
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.152
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0210.022
Science and technology studies0.0030.004
Scholarly communication0.0070.009
Open science0.0020.004
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.058
GPT teacher head0.410
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreReview

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".

Quick stats

Citations18
Published2023
Admission routes3
Has abstractyes

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