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Record W2155641954 · doi:10.1093/pch/10.6.335

Use of anencephalic newborns as organ donors

2005· article· en· W2155641954 on OpenAlexaboutno aff
Paul Byrne

Bibliographic record

VenuePaediatrics & Child Health · 2005
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

Organ transplantation for infants and children with lifethreatening illnesses has become very successful. However, this success is limited by a serious shortage of suitable donor organs. A variety of approaches to improve organ donation rates have been undertaken in adults but these approaches cannot be applied widely to paediatric patients because of physical limitations governing organ suitability and size. These limitations caused widespread discussion in the late 1980s and 1990s about considering the anencephalic infant as an organ donor, including the possibility of altering the standard brain death criteria to apply to the anencephalic in­ fant and of donation of anencephalic infant organs before death [1]-[5]. The potential to save the lives of infants dying from cardiac, renal and liver disease, and the desire to give meaning and benefit to the anencephalic infant’s family were presented as justification for changes in the medical standards and the law concerning death and organ donation from anen­ cephalic infants [6][7]. Official statements from the Canadian Paediatric Society (CPS) (1990) and the American Academy of Pediatrics (1992) affirmed that anencephalic infants were not appropriate organ donors and rejected arguments advo­ cating modification of the medical criteria of brain death and legal standards of pronouncement of death [8][9]. This updated CPS statement presents current information for clinicians supporting the previous CPS position that did not support the use of anencephalic infants as organ donors in the clini­ cal setting.

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 score0.118
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
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.288
Teacher spread0.267 · 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

Citations31
Published2005
Admission routes1
Has abstractyes

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