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Record W3191989589 · doi:10.1097/tp.0000000000003919

Cardiometabolic and Kidney Protection in Kidney Transplant Recipients With Diabetes: Mechanisms, Clinical Applications, and Summary of Clinical Trials

2021· article· en· W3191989589 on OpenAlexafffund

Bibliographic record

VenueTransplantation · 2021
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
FundersBanting and Best Diabetes Centre, University of TorontoCanadian Institutes of Health ResearchKU LeuvenDepartment of Medicine, University of TorontoNovo NordiskSanofiUniversity of TorontoDiabetes CanadaAstraZenecaEli Lilly and Company
KeywordsDiabetes mellitusClinical trialKidneyKidney diseaseKidney transplantationTransplantationDisease

Abstract

fetched live from OpenAlex

Kidney transplantation is the therapy of choice for patients with end-stage renal disease. Preexisting diabetes is highly prevalent in kidney transplant recipients (KTR), and the development of posttransplant diabetes is common because of a number of transplant-specific risk factors such as the use of diabetogenic immunosuppressive medications and posttransplant weight gain. The presence of pretransplant and posttransplant diabetes in KTR significantly and variably affect the risk of graft failure, cardiovascular disease (CVD), and death. Among the many available therapies for diabetes, there are little data to determine the glucose-lowering agent(s) of choice in KTR. Furthermore, despite the high burden of graft loss and CVD among KTR with diabetes, evidence for strategies offering cardiovascular and kidney protection is lacking. Recent accumulating evidence convincingly shows glucose-independent cardiorenal protective effects in non-KTR with glucose-lowering agents, such as sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists. Therefore, our aim was to review cardiorenal protective strategies, including the evidence, mechanisms, and rationale for the use of these glucose-lowering agents in KTR with diabetes.

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)0.002
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 score0.036
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.086
GPT teacher head0.393
Teacher spread0.307 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

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