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Record W7140067473

Empagliflozin Increases Cardiac Energy Production in Diabetes: Novel Translational Insights Into the Heart Failure Benefits of SGLT2 Inhibitors

2018· article· en· W7140067473 on OpenAlexfundno aff
Subodh Verma, Sonia Rawat, Michael Lehrke, Nikolaus Albert Helmut Marx, Gary D. Lopaschuk, Kim L. Ho, Cory S. Wagg, Liyan Zhang, Hwee Teoh, John E. Dyck, Golam M. Uddin, Gavin Y. Oudit, Eric Mayoux

Bibliographic record

VenueRWTH Publications (RWTH Aachen) · 2018
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsnot available
FundersNational Institutes of HealthServierGlaxoSmithKlineVerily Life SciencesPatient-Centered Outcomes Research InstituteAstraZenecaEli Lilly and CompanyBristol-Myers SquibbAmerican College of Cardiology FoundationAmgenAstraZeneca HealthCare FoundationSanofiCanadian Institutes of Health ResearchAmerican Heart Association
KeywordsEmpagliflozinHeart failureBenzhydryl compoundsEnergy metabolismDiabetes mellitusProduction (economics)
DOInot available

Abstract

fetched live from OpenAlex

Upon completion of this activity, the learner should be able to: 1) discuss the effects of treatment with empagliflozin on cardiac energy production; 2) examine the guideline recommendations for treatment of diabetes in patients with or at risk for cardiovascular disease; and 3) define the potential cardiovascular benefits associated with the treatment of diabetes mellitus with empaglifozin

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.014
GPT teacher head0.238
Teacher spread0.224 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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