MétaCan
Menu
Back to cohort
Record W2981496710 · doi:10.1210/endrev/bnz010

The New Biology of Diabetic Kidney Disease—Mechanisms and Therapeutic Implications

2019· review· en· W2981496710 on OpenAlexaff
Yuliya Lytvyn, Petter Bjornstad, Daniël H. van Raalte, Hiddo J.L. Heerspink, David Z.I. Cherney

Bibliographic record

VenueEndocrine Reviews · 2019
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsToronto General HospitalUniversity of Toronto
FundersColorado Clinical and Translational Sciences InstituteCenter for Women’s Health Research, University of ColoradoInternational Society for Pediatric and Adolescent DiabetesJuvenile Diabetes Research Foundation United States of AmericaNational Institutes of HealthNational Institute of Diabetes and Digestive and Kidney DiseasesThrasher Research Fund
KeywordsDiabetes mellitusMedicineKidney diseaseDiseaseIntensive care medicineKidneyPathogenesisStroke (engine)Type 2 diabetesMyocardial infarctionInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Diabetic kidney disease remains the most common cause of end-stage kidney disease in the world. Despite reductions in incidence rates of myocardial infarction and stroke in people with diabetes over the past 3 decades, the risk of diabetic kidney disease has remained unchanged, and may even be increasing in younger individuals afflicted with this disease. Accordingly, changes in public health policy have to be implemented to address the root causes of diabetic kidney disease, including the rise of obesity and diabetes, in addition to the use of safe and effective pharmacological agents to prevent cardiorenal complications in people with diabetes. The aim of this article is to review the mechanisms of pathogenesis and therapies that are either in clinical practice or that are emerging in clinical development programs for potential use to treat diabetic kidney disease.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.069
GPT teacher head0.373
Teacher spread0.303 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations168
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueEndocrine ReviewsSame topicDiabetes Treatment and ManagementFrench-language works237,207