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Record W4200138320 · doi:10.1097/mot.0000000000000946

The use of marginal kidneys in dual kidney transplantation to expand kidney graft utilization

2021· review· en· W4200138320 on OpenAlexaff
Robert J. Stratta, David Harriman, Venkat Gurram, Komal Gurung, B Sharda

Bibliographic record

VenueCurrent Opinion in Organ Transplantation · 2021
Typereview
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineKidneyKidney transplantationTransplantationNephronIntensive care medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The purpose of this review is to chronicle the history of dual kidney transplantation (DKT) and identify opportunities to improve utilization of marginal deceased donor (MDD) kidneys through DKT. RECENT FINDINGS: The practice of DKT from adult MDDs dates back to the mid-1990s, at which time the primary indication was projected insufficient nephron mass from older donors. Multiple subsequent studies of short- and long-term success have been reported focusing on three major aspects: Identifying appropriate selection criteria/scoring systems based on pre- and postdonation factors; refining technical aspects; and analyzing longer-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. MDDs with limited renal functional capacity represent a large proportion of potential kidneys doomed to either discard or nonrecovery. SUMMARY: DKT may reduce organ discard and optimize the use of kidneys from MDDs. New and innovative technologies targeting ex vivo organ assessment, repair, and regeneration may have a major impact on the decision whether or not to use recovered kidneys for single or 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.763
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.217
GPT teacher head0.423
Teacher spread0.206 · 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 designNot applicable
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

Citations4
Published2021
Admission routes1
Has abstractyes

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