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The Use of Executed Prisoners as a Source of Organ Transplants in China Must Stop

2011· article· en· W1535268337 on OpenAlexaboutno aff
Gabriel M. Danovitch, Michael Shapiro, Jacob Lavee

Bibliographic record

VenueAmerican Journal of Transplantation · 2011
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineChinaOrgan donationChristian ministryDonationTransplantationClinical PracticeMedical ethicsOrgan transplantationLawSurgeryFamily medicinePolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

Internationally accepted ethical standards are unequivocal in their prohibition of the use of organs recovered from executed prisoners: yet this practice continues in China despite indications that Ministry of Health officials intend to end this abhorrent practice. Recently published articles on this topic emphasize the medical complications that result from liver transplantation from executed 'donors' but scant attention is given to the source of the organs, raising concern that the transplant community may be becoming inured to unacceptable practice. Strategies to influence positive change in organ donation practice in China by the international transplant community are discussed. They include an absolutist policy whereby no clinical data from China is deemed acceptable until unacceptable donation practices end, and an incremental policy whereby clinical data is carefully evaluated for acceptability. The relative advantages and drawbacks of these strategies are discussed together with some practical suggestions for response available to individuals and the transplant community.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.020
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.010
Scholarly communication0.0050.004
Open science0.0020.005
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.255
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations30
Published2011
Admission routes1
Has abstractyes

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