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MP52-02 INITIAL EXPERIENCE WITH RENAL TRANSPLANTS AFTER MEDICAL ASSISTANCE IN DYING: FIRST SERIES IN NORTH AMERICA

2020· article· en· W3021483862 on OpenAlexaboutno aff
Samir Sami, Max A. Levine, Andrew Rasmussen, Alp Şener, Patrick Luke

Bibliographic record

VenueThe Journal of Urology · 2020
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTransplantationLegislationOrgan procurementSurgeryGeneral surgeryLaw

Abstract

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You have accessJournal of UrologyTransplantation & Vascular Surgery: Renal Transplantation & Vascular Surgery II (MP52)1 Apr 2020MP52-02 INITIAL EXPERIENCE WITH RENAL TRANSPLANTS AFTER MEDICAL ASSISTANCE IN DYING: FIRST SERIES IN NORTH AMERICA Samir Sami*, Max Levine, Andrew Rasmussen, Alp Sener, and Patrick Luke Samir Sami*Samir Sami* More articles by this author , Max LevineMax Levine More articles by this author , Andrew RasmussenAndrew Rasmussen More articles by this author , Alp SenerAlp Sener More articles by this author , and Patrick LukePatrick Luke More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000914.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: In 2016, Canadian federal legislation was passed creating a regulatory framework for medical assistance in dying (MAiD) for individuals who were suffering from a medically futile condition and foreseeable death. As there is a paucity of literature on transplantation from MAiD donors, we report our unique experience and first reported outcomes in North America. METHODS: We retrospectively analyzed all renal transplant recipients from MAiD donors at London Health Sciences Centre, beginning in 2018. Patients eligible for MAiD underwent circulatory death after administration of life ending therapy and organ procurement was performed as per standard protocol. Ethics approval was obtained to review these outcomes. RESULTS: There were a total of 4 patients who became kidney donors and one kidney-pancreas donor. The indication for MAiD in the donors included 3 with debilitating neurological disease, 1 with heart failure and 1 who previously suffered a significant fall resulting in quadriplegia. Mean donor age was 53.66 ±13.17, median warm ischemia time (WIT) was 17 minutes, and median cold ischemia time (CIT) was 9 hours. One perioperative death occurred due to medical complication unrelated to renal graft function. No delayed graft function was encountered and only two patients experienced slow graft function. Median 30 day creatinine was 108 μmol. CONCLUSIONS: MAiD associated organ donation represents a potential means to increase the donor pool for those awaiting deceased donor organs. The controlled nature of MAiD associated circulatory death has the potential to minimize the deleterious effects of prolonged WIT that may be associated with standard donation after circulatory death. Our institution’s early experience is encouraging, with low WIT, and favorable early graft function results. Ongoing assessment of MAiD outcomes is required to better quantify the quality of these donation opportunities compared to conventional deceased donors. Source of Funding: N/A © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e772-e772 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Samir Sami* More articles by this author Max Levine More articles by this author Andrew Rasmussen More articles by this author Alp Sener More articles by this author Patrick Luke More articles by this author Expand All Advertisement PDF downloadLoading ...

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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 designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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