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Novel Drugs for Diabetes Also Have Dramatic Benefits on Hard Outcomesof Heart and Kidney Disease

2022· review· en· W4280518784 on OpenAlexaff
Michael Chan, Jonathan C.H. Chan

Bibliographic record

VenueCurrent Cardiology Reviews · 2022
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineDiabetes mellitusType 2 diabetesDiseaseIntensive care medicineClinical trialCanagliflozinKidney diseaseInternal medicineDiabetologySemaglutideLiraglutideEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Diabetes is a major risk factor for developing cardiovascular disease. Patients with both diabetes and cardiovascular disease have even higher mortality. The convergence of cardiology and diabetology therapy is an important step in treating patients and advancing research. RESULTS: Major landmark trials and meta-analyses involving Sodium Glucose Cotransporter 2 inhibitors have shown dramatic clinical cardiorenal benefits in patients both with and without type 2 diabetes. In type 2 diabetes patients, Glucagon-like peptide-1 receptor agonists have been shown to improve major cardiac outcomes. CONCLUSION: This hot topic of research and clinical use of glucose lowering drugs intersects the fields of cardiovascular, renal, and diabetic medicine. The numerous cardiorenal benefits have led to the rapid adoption in clinical guidelines of these glucose lowering drugs in patients with Type 2 diabetes, cardiovascular disease, or renal 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 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.139
GPT teacher head0.376
Teacher spread0.237 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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