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Record W4200424855 · doi:10.1111/ctr.14566

Dual kidney transplants from adult marginal donors: Review and perspective

2021· review· en· W4200424855 on OpenAlexaff
Robert J. Stratta, David Harriman, Venkat Gurram, Komal Gurung, B Sharda

Bibliographic record

VenueClinical Transplantation · 2021
Typereview
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineUnited Network for Organ SharingOrgan donationTransplantationIntensive care medicineGerontologySurgeryLiver transplantation

Abstract

fetched live from OpenAlex

The practice of dual kidney transplantation (DKT) from adult marginal deceased donors (MDDs) dates back to the mid-1990s with initial pioneering experiences reported by the Stanford and Maryland groups, at which time the primary indication was estimated insufficient nephron mass from older donors. Multiple subsequent studies of short and long-term success have been reported focusing on three major aspects of DKT: Identifying appropriate selection criteria and developing scoring systems based on pre- and post-donation factors; refining technical aspects; and analyzing mid-term outcomes. The number of adult DKTs performed in the United States has declined in the past decade and only about 60 are performed annually. For adult deceased donor kidneys meeting double allocation criteria, > 60% are ultimately not transplanted. Deceased donors with limited renal functional capacity represent a large proportion of potential kidneys doomed to either discard or non-recovery. However, DKT may reduce organ discard and optimize the use of kidneys from MDDs. In an attempt to promote utilization of MDD kidneys, the United Network for Organ Sharing introduced new allocation guidelines pursuant to DKT in 2019. The purpose of this review is to chronicle the history of DKT and identify opportunities to improve utilization of MDD kidneys through DKT.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.550
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.105
GPT teacher head0.457
Teacher spread0.352 · 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.

Study designSystematic review
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

Citations9
Published2021
Admission routes1
Has abstractyes

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