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Record W2103196465 · doi:10.1111/ajt.13232

Living and Deceased Organ Donation Should Be Financially Neutral Acts

2015· article· en· W2103196465 on OpenAlexaff
Francis L. Delmonico, Dominique Martin, Beatriz Domínguez‐Gil, Elmi Muller, Vivekanand Jha, Adeera Levin, Gabriel M. Danovitch, Alexander Morgan Capron

Bibliographic record

VenueAmerican Journal of Transplantation · 2015
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOrgan donationEconomic shortageDonationMedicineIncentiveKidney donationArgument (complex analysis)Waiting listTransplantationFinanceKidney transplantationBusinessSurgeryEconomicsEconomic growthInternal medicineMarket economy

Abstract

fetched live from OpenAlex

The supply of organs—particularly kidneys—donated by living and deceased donors falls short of the number of patients added annually to transplant waiting lists in the United States. To remedy this problem, a number of prominent physicians, ethicists, economists and others have mounted a campaign to suspend the prohibitions in the National Organ Transplant Act of 1984 (NOTA) on the buying and selling of organs. The argument that providing financial benefits would incentivize enough people to part with a kidney (or a portion of a liver) to clear the waiting lists is flawed. This commentary marshals arguments against the claim that the shortage of donor organs would best be overcome by providing financial incentives for donation. We can increase the number of organs available for transplantation by removing all financial disincentives that deter unpaid living or deceased kidney donation. These disincentives include a range of burdens, such as the costs of travel and lodging for medical evaluation and surgery, lost wages, and the expense of dependent care during the period of organ removal and recuperation. Organ donation should remain an act that is financially neutral for donors, neither imposing financial burdens nor enriching them monetarily.

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: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.409

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 head0.035
GPT teacher head0.294
Teacher spread0.259 · 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

Citations82
Published2015
Admission routes1
Has abstractyes

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