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Record W2916824239 · doi:10.1016/j.jacbts.2018.11.010

Empagliflozin Improves Diastolic Function in a Nondiabetic Rodent Model of Heart Failure With Preserved Ejection Fraction

2019· article· en· W2916824239 on OpenAlexaff
Kim A. Connelly, Yanling Zhang, Aylin Visram, Andrew Advani, Sri Nagarjun Batchu, Jean‐François Desjardins, Kerri Thai, Richard E. Gilbert

Bibliographic record

VenueJACC Basic to Translational Science · 2019
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsEmpagliflozinMedicineCardiologyHeart failureEjection fractionPreloadAfterloadInternal medicineRanolazineHeart failure with preserved ejection fractionRodent modelDiastoleGlycemicHemodynamicsDiabetes mellitusType 2 diabetesBlood pressureInsulinEndocrinology

Abstract

fetched live from OpenAlex

Recent studies send an unambiguous signal that the class of agents known as sodium-glucose-linked co-transporter-2 inhibitors (SGLT2i) prevent heart failure hospitalization in patients with type 2 diabetes. However, the mechanisms remain unclear. Herein the authors utilize a rodent model of heart failure with preserved ejection fraction (HFpEF), and demonstrate that treatment with the SGLT2i empagliflozin, reduces left ventricular mass, improving both wall stress and diastolic function. These findings extend the observation that the main mechanism of action of empagliflozin involves improved hemodynamics (i.e., reduction in preload and afterload) and provide a rationale for upcoming trials in patients with HFpEF irrespective of glycemic status.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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 designBench or experimental
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

Citations120
Published2019
Admission routes1
Has abstractyes

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