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Optimizing Organ Donation After Euthanasia: A Critical Appraisal

2025· article· en· W4409713427 on OpenAlexaboutno aff
E. A. J. Alkemade, Hwai‐Ding Lam, Bart Hendriks, Andries E. Braat, Ian P.J. Alwayn, Minneke J. Coenraad, Andrzej Baranski

Bibliographic record

VenueTransplantology · 2025
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsOrgan donationCritical appraisalMedicinePsychologyIntensive care medicineSurgeryTransplantationAlternative medicinePathology

Abstract

fetched live from OpenAlex

This Critical Appraisal aims to explore the pharmacokinetics and pharmacodynamics of medications used in organ donors after euthanasia (ODE) and their impact on abdominal organ quality. With the legalization of ODE, the donor pool has expanded, but it has introduced complexities regarding organ quality. This study evaluates existing euthanasia protocols in the Netherlands, Belgium, Spain, and Canada, focusing on differences in the medication types and dosages. Additionally, a literature review assessed the potential hepatotoxic effects of high-dose medications like thiopental, propofol, and non-depolarizing neuromuscular blocking agents. High doses of non-depolarizing neuromuscular blocking agents, particularly rocuronium, are associated with hepatotoxic effects in vitro. Furthermore, thiopental doses exceeding 750 mg significantly increase the risk of liver dysfunction. Recent findings also indicate that high-dose propofol and lidocaine can slightly prolong the time to death, which is crucial for optimizing organ viability in ODE. This study highlights the need to optimize organ donation procedures after euthanasia. Further research is needed to achieve this balance, maintaining the integrity and ethical standards of the euthanasia process while enhancing the outcomes of organ donation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.662
Threshold uncertainty score0.507

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)0.0000.000

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 head0.017
GPT teacher head0.355
Teacher spread0.337 · 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 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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