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Record W2607226802 · doi:10.1097/tp.0000000000001778

Kidney Paired Donation and the “Valuable Consideration” Problem

2017· review· en· W2607226802 on OpenAlexaffabout
Maeghan Toews, Mark Giancaspro, Bernadette Richards, Paolo Ferrari

Bibliographic record

VenueTransplantation · 2017
Typereview
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPrinciple of legalityOrgan donationDonationCLARITYLegislatureLawTransplantationMedicinePolitical scienceBusinessLaw and economicsSociologySurgery

Abstract

fetched live from OpenAlex

As organ donation rates remain unable to meet the needs of individuals waiting for transplants, it is necessary to identify reasons for this shortage and develop solutions to address it. The introduction of kidney paired donation (KPD) programs represents one such innovation that has become a valuable tool in donation systems around the world. Although KPD has been successful in increasing kidney donation and transplantation, there are lingering questions about its legality. Donation through KPD is done in exchange for-and with the expectation of-a reciprocal kidney donation and transplantation. It is this reciprocity that has caused concern about whether KPD complies with existing law. Organ donation systems around the world are almost universally structured to legally prohibit the commercial exchange of organs. Australia, Canada, and the United States have accomplished this goal by prohibiting the exchange of an organ for "valuable consideration," which is a legal term that has not historically been limited to monetary exchange. Whether or not KPD programs violate this legislative prohibition will depend on the specific legislative provision being considered, and the legal system and case law of the particular jurisdiction in question. This article compares the experiences of Australia, Canada, and the United States in determining the legality of KPD and highlights the need for legal clarity and flexibility as donation and transplantation systems continue to evolve.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.101
GPT teacher head0.372
Teacher spread0.271 · 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 designNot applicable
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

Citations14
Published2017
Admission routes2
Has abstractyes

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