MétaCan
Menu
Back to cohort
Record W4391144447 · doi:10.1097/tp.0000000000004894

The American Society of Transplant Surgeons Consensus Statement on Normothermic Regional Perfusion

2024· article· en· W4391144447 on OpenAlexaff
Anji Wall, Bradley L. Adams, Aleah L. Brubaker, Cherylee W. J. Chang, Kristopher P. Croome, Jennifer Frontera, Elisa J. Gordon, Jordan Hoffman, Lewis J. Kaplan, Deepali Kumar, Josh Levisky, Eduardo Miñambres, Brendan Parent, Christopher J.E. Watson, Ajmal Zemmar, Elizabeth A. Pomfret

Bibliographic record

VenueTransplantation · 2024
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsDonationMedicineOrgan procurementConsensus conferenceTransplantationStatement (logic)Process (computing)Organ donationFunction (biology)Intensive care medicineSurgeryPolitical scienceInternal medicineLawComputer science

Abstract

fetched live from OpenAlex

On June 3, 2023, the American Society of Transplant Surgeons convened a meeting in San Diego, California to (1) develop a consensus statement with supporting data on the ethical tenets of thoracoabdominal normothermic regional perfusion (NRP) and abdominal NRP; (2) provide guidelines for the standards of practice that should govern thoracoabdominal NRP and abdominal NRP; and (3) develop and implement a central database for the collection of NRP donor and recipient data in the United States. National and international leaders in the fields of neuroscience, transplantation, critical care, NRP, Organ Procurement Organizations, transplant centers, and donor families participated. The conference was designed to focus on the controversial issues of neurological flow and function in donation after circulatory death donors during NRP and propose technical standards necessary to ensure that this procedure is performed safely and effectively. This article discusses major topics and conclusions addressed at the meeting.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.388

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 head0.025
GPT teacher head0.312
Teacher spread0.287 · 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 designBench or experimental
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

Citations46
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueTransplantationSame topicOrgan Transplantation Techniques and OutcomesFrench-language works237,207