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Record W3128709333 · doi:10.1161/circep.120.009115

Metformin Is Associated With a Lower Risk of Atrial Fibrillation and Ventricular Arrhythmias Compared With Sulfonylureas

2021· article· en· W3128709333 on OpenAlexaff
Anna Ostropolets, Pierre Elias, Michael V. Reyes, Elaine Y. Wan, Utpal B. Pajvani, George Hripcsak, John Morrow

Bibliographic record

VenueCirculation Arrhythmia and Electrophysiology · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsColumbia College
FundersU.S. National Library of MedicineNational Heart, Lung, and Blood Institute
KeywordsAtrial fibrillationMetforminCardiologyInternal medicineMedicineInsulin

Abstract

fetched live from OpenAlex

Background: Type 2 diabetes is one of the most common chronic disorders worldwide and is an important cause of cardiovascular disease. Studies investigating the risk of atrial and ventricular arrhythmias in diabetic patients taking different oral diabetes medications are sparse. Methods: We used IBM MarketScan Medicare Supplemental Database to examine the risk of arrhythmias for patients on different oral diabetes medications by propensity score matching. Results: We found that patients on metformin monotherapy had significantly reduced risk of atrial arrhythmias, including atrial fibrillation, compared with monotherapy with DPP4 (dipeptidyl peptidase 4 inhibitor) or thiazolidinedione medications. Patients on metformin monotherapy had significantly reduced risk of atrial arrhythmias, ventricular arrhythmias, and bradycardia compared with monotherapy with sulfonylureas. Combination therapy with sulfonylureas and metformin had an increased risk of atrial arrhythmias compared with some other combinations. Conclusions: Different oral diabetes medications have significantly different long-term risk of arrhythmia. Specifically, metformin is associated with reduced risk of atrial fibrillation and ventricular arrhythmias compared with sulfonylureas.

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 score0.191
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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

Citations52
Published2021
Admission routes1
Has abstractyes

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