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Record W7062223281

Tug Of War Between Opt-In And Opt-out Organ Donation Systems

2025· article· en· W7062223281 on OpenAlexaboutno aff

Bibliographic record

VenueThe Institutional Repository at DePaul University (DePaul University) · 2025
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsnot available
Fundersnot available
KeywordsOrgan donationTug of warDonationOrgan transplantationOrgan systemOpt-outUnited Network for Organ Sharing
DOInot available

Abstract

fetched live from OpenAlex

The demand for organ transplants is far greater than the supply of transplantable organs. Every day, twenty people in the United States die as they await an organ transplant; this equates to roughly 7,300 people annually. Whilst organ donation can be highly effective for prospective patients, not all individuals want to take part in such an action. A person’s decision not to partake in organ donation can stem from their ethics, morality, religion, and much more. For individuals that live in the United States and do not want to donate their organs upon death, they do not have to take any affirmative actions to ensure this result because the United States is an opt-in system. Opt-in policies require an individual to “manifestly express their preferences for being a deceased organ donor.” Generally, an individual’s organs will not be donated unless the individual has expressly stated that they would like to ‘opt-in’ to donating their organs upon death. Alternatively, some nations utilize an opt-out system; this system presumes that everyone is a willing donor unless they “specifically ‘opt-out’ of doing so.” Within the past few years, several nations including Iceland, England, Scotland, Canada, and the Netherlands have switched to an opt-out system. It may be a common assumption that opt-out systems should generate more successful organ transplants since everyone would presumably be a willing donor, and because this system expands the pool of potential donors to include nearly all citizens. In Peter Singer’s example, he tells us to imagine walking past a pond and seeing a child about to drown where there is no one else readily able to help the child. Singer believes that there is a duty to rescue the child since it is “neither difficult nor dangerous” and because the benefit to the child outweighs any costs that may incur to the rescuer; this would be known as an easy rescue. However, Singer’s example of having a duty to rescue may take a different shape when within the realm of organ donation. Those in need of a new organ can be complicated recipients due to their specific medical complexities and/or conditions. Further, some argue that donees incur little costs; they argue that “donating would not clash with any of the people’s important values, beliefs, preferences, or projects.” However, can a transaction including an organ really be considered an easy rescue? Is there both a duty to save a drowning child and a duty to give your organs to that child upon your death? This article will explore the opt-in and opt-out systems within the organ donation field, and how these different systems have manifested themselves in different nations. It will be revealed that opt-in and opt-out systems do not differ much from one another; both systems come with their own respective tradeoffs.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.007
GPT teacher head0.178
Teacher spread0.171 · 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 designSimulation or modeling
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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