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SGLT2 Inhibitors: The Sweet Success for Kidneys

2023· review· en· W4318262703 on OpenAlex
Atit Dharia, Abid Ali Khan, Vikas S. Sridhar, David Z.I. Cherney

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnnual Review of Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersDepartment of Medicine, University of TorontoCanadian Institutes of Health ResearchBanting and Best Diabetes Centre, University of TorontoKidney Foundation of CanadaUniversity of TorontoDiabetes Canada
KeywordsAlbuminuriaMedicineRenal functionKidneyDiabetes mellitusClinical trialEmpagliflozinKidney diseaseIntensive care medicineInternal medicineType 2 diabetesEndocrinology

Abstract

fetched live from OpenAlex

Sodium-glucose cotransporter-2 inhibitors (SGLT2 inhibitors) were originally developed as antidiabetic agents, with cardiovascular (CV) outcome trials demonstrating improved CV outcomes in patients with type 2 diabetes mellitus (T2D). Secondary analyses of CV outcome trials and later dedicated kidney outcome trials consistently reported improved kidney-related outcomes independent of T2D status and across a range of kidney function and albuminuria. Importantly, SGLT2 inhibitors are generally safe and well tolerated, with clinical trials and real-world analyses demonstrating a decrease in the risk of acute kidney injury. The kidney protective effects of SGLT2 inhibitors generally extend across different members of the class, possibly on the basis of hemodynamic, metabolic, anti-inflammatory, and antifibrotic mechanisms. In this review, we summarize the effects of SGLT2 inhibitors on kidney outcomes in diverse patient populations.

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.

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)0.002
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 score0.407
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
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 head0.060
GPT teacher head0.405
Teacher spread0.345 · 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