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Record W4214854497 · doi:10.1097/tp.0000000000004098

Normothermic Ex Vivo Kidney Perfusion for Human Kidney Transplantation: First North American Results

2022· article· en· W4214854497 on OpenAlexaff

Bibliographic record

VenueTransplantation · 2022
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity of TorontoToronto General HospitalHospital for Sick ChildrenUniversity Health Network
Fundersnot available
KeywordsHuman kidneyEx vivoKidneyPerfusionKidney transplantationKidney disease

Abstract

fetched live from OpenAlex

BACKGROUND: Normothermic ex vivo kidney perfusion (NEVKP) has shown promising results for preservation, assessment, and reconditioning of kidney allografts in preclinical studies. Here, we report the first North American safety and feasibility study of deceased donor kidneys grafts transplanted following preservation with NEVKP. METHODS: Outcomes of 13 human kidney grafts that received 1 to 3 h of NEVKP after being transported in an anoxic hypothermic machine perfusion device were compared with a matched control group of 26 grafts that were preserved with anoxic hypothermic machine perfusion alone. RESULTS: Grafts were perfused for a median of 171 min (range, 44-275 min). The delayed graft function rate in NEVKP versus control patients was 30.8% versus 46.2% ( P = 0.51). During the 1-y follow-up, no differences in postoperative graft function, measured by serum creatinine, necessity for dialysis, and urine production, were found between the study group and the control group. There were no differences in 1 y posttransplantation graft or patient survival between the 2 groups. CONCLUSIONS: Our study demonstrates the safety and feasibility of NEVKP for human deceased donor kidney transplantation. Further studies are warranted to explore how this technology can minimize cold ischemia, improve posttransplant graft function, and assess and repair expanded criteria kidney grafts.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.277
Teacher spread0.262 · 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 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

Citations65
Published2022
Admission routes1
Has abstractyes

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