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Record W4390699892 · doi:10.1136/medhum-2023-012772

Personalism and boosting organ ResERVOirs: a consideration of euthanasia by removal of vital organs in the Canadian context

2024· article· en· W4390699892 on OpenAlexaffabout
Jamie Grunwald

Bibliographic record

VenueMedical Humanities · 2024
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsLeduc Community HospitalPrince Albert Grand CouncilUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsBioethicsDignityConscienceOrgan donationPersonalismContext (archaeology)Assisted suicideMedicineEnvironmental ethicsLawSociologyPsychologyTransplantationPsychiatrySurgeryPhilosophyPolitical scienceHistory

Abstract

fetched live from OpenAlex

Canada's decriminalisation of assisted death has elicited significant ethical implications for the use of assisted death in healthcare contexts. Euthanasia by removal of vital organs (ERVO) is a theoretical extension of medically assisted death with an increased plausibility of implementation in light of the rapid expansion of assisted death eligibility laws and criteria in Canada. ERVO entails removing organs from a living patient under general anaesthesia as the mechanism of death. While ERVO is intended to maximise the viability of organs procured from the euthanised patient for donation to recipients, ending the lives of patient donors in this manner solely to benefit ill or dying recipient patients merits further ethical consideration. Specifically, the paper explores the application of personalist bioethics in determining whether the means of procuring organs through assisted death justifies the end of improving the lives of those who would benefit from receiving them. Further, by discussing the medical, social and ethical implications of ERVO, I will explicate a broader philosophical understanding of the influences of legalising assisted death on human dignity and conscience.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.041
GPT teacher head0.289
Teacher spread0.248 · 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 teacher head, not a consensus.

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

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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