MétaCan
Menu
Back to cohort
Record W4312095491 · doi:10.1080/14779072.2023.2159810

The anti-hypertensive effects of sodium-glucose cotransporter-2 inhibitors

2022· review· en· W4312095491 on OpenAlexaff
Luxcia Kugathasan, Lisa Dubrofsky, Andrew Advani, David Z.I. Cherney

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2022
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSt. Michael's HospitalSunnybrook Health Science CentreWomen's College HospitalUniversity Health NetworkUniversity of TorontoHealth Sciences Centre
Fundersnot available
KeywordsMedicineNatriuresisDiabetes mellitusBlood pressureDiureticKidney diseaseEmpagliflozinInternal medicineCanagliflozinHeart failureClinical trialType 2 diabetesLoop diureticCardiologyEndocrinologyHemodynamicsDiseasePharmacology

Abstract

fetched live from OpenAlex

INTRODUCTION: Hypertension is a well-established risk factor for cardiovascular (CV) events in patients with chronic kidney disease (CKD), heart failure, obesity, and diabetes. Despite the usual prescribed antihypertensive therapies, many patients fail to achieve the recommended blood pressure (BP) targets. AREAS COVERED: This review summarizes the clinical BP-lowering data presented in major CV and kidney outcome trials for sodium-glucose cotransporter-2 (SGLT2) inhibitors, as well as smaller dedicated BP trials in high-risk individuals with and without diabetes. We have also highlighted potential mechanisms that may contribute to the antihypertensive effects of SGLT2 inhibitors, including natriuresis and hemodynamic changes, a loop diuretic-like effect, and alterations in vascular physiology. EXPERT OPINION: The antihypertensive properties of SGLT2 inhibitors are generally modest but may be larger in certain patient populations. SGLT2 inhibitors may have an additional role as an adjunctive BP-lowering therapy in patients with hypertension at high risk of CV disease or 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.888
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0080.010
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 head0.023
GPT teacher head0.298
Teacher spread0.275 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueExpert Review of Cardiovascular TherapySame topicDiabetes Treatment and ManagementFrench-language works237,207