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Record W2965687553 · doi:10.1016/j.pcad.2019.07.005

Mechanistic insights regarding the role of SGLT2 inhibitors and GLP1 agonist drugs on cardiovascular disease in diabetes

2019· review· en· W2965687553 on OpenAlex

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.

Bibliographic record

VenueProgress in Cardiovascular Diseases · 2019
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineDiabetes mellitusHeart failureDiseaseType 2 diabetesType 2 Diabetes MellitusAdverse effectPharmacologyInternal medicineBioinformaticsEndocrinology

Abstract

fetched live from OpenAlex

The treatment landscape for patients with established or at high risk for cardiovascular disease and type 2 diabetes mellitus has entirely changed over the past decade, with the introduction of several anti-hyperglycemic agents. Sodium-glucose cotransporter 2 (SGLT2) inhibitors and glucagon-like peptide-1 (GLP-1) agonists are two anti-hyperglycemic classes which have been of special interest after multiple large cardiovascular disease (CVD) outcomes studies have demonstrated superiority of these agents compared to placebo for major adverse CVD events and in some cases, hospitalization for heart failure. Despite the dramatic results of these trials, only recently have we began to understand the mechanisms underlying these CVD benefits. Here we review the underlying mechanisms which have the greatest plausibility for both of these agents including the impact of ventricular loading conditions, direct effects on cardiac structure and function, myocardial energetics and sodium/hydrogen exchange for SGLT2 inhibitors, and the anti-atherosclerotic, anti-inflammatory, and modulation of endothelial function for GLP-1 agonists.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
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 head0.015
GPT teacher head0.260
Teacher spread0.245 · 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