MétaCan
Menu
Back to cohort
Record W3155932096 · doi:10.3803/enm.2021.987

Cardiorenal Protection in Diabetic Kidney Disease

2021· review· en· W3155932096 on OpenAlexafffund
Jason F. Lee, Ecaterina Berzan, Vikas S. Sridhar, Ayodele Odutayo, David Z.I. Cherney

Bibliographic record

VenueEndocrinology and Metabolism · 2021
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSt. Michael's HospitalToronto General HospitalDiabetes CanadaUniversity of TorontoUniversity Health Network
FundersBanting and Best Diabetes Centre, University of TorontoCanadian Institutes of Health ResearchDepartment of Medicine, University of TorontoUniversity of TorontoDiabetes Canada
KeywordsMedicineKidney diseaseMineralocorticoid receptorDiabetes mellitusType 2 diabetesKidneyDiseasePopulationInternal medicineDulaglutideIntensive care medicineEndocrinologyLiraglutideAldosterone

Abstract

fetched live from OpenAlex

Over the last 5 years there have been many new developments in the management of diabetic kidney disease. Glucagon-like peptide-1 receptor agonists (GLP-1 RA) and sodium-glucose cotransporter-2 (SGLT2) inhibitors were initially used for glycemic control, but more recent studies have now shown that their benefits extend to cardiovascular and kidney outcomes. The recent addition of data on the novel mineralocorticoid receptor antagonist (MRA) gives us another approach to further decrease the residual risk of diabetic kidney disease progression. In this review we describe the mechanism of action, key studies, and possible adverse effects related to these three classes of medications. The management of type 2 diabetes now includes an increasing number of medications for the management of comorbidities in a patient population at significant risk of cardiovascular disease and progression of chronic kidney disease. It is from this perspective that we seek to outline the rationale for the sequential and/or combined use of SGLT2 inhibitors, GLP-1 RA and MRAs in patients with type 2 diabetes for heart and kidney protection.

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 score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.031
GPT teacher head0.300
Teacher spread0.269 · 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

Citations19
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueEndocrinology and MetabolismSame topicDiabetes Treatment and ManagementFrench-language works237,207