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Do Dipeptidyl Peptidase-4 Inhibitors Increase the Risk of Heart Failure inPatients with Type 2 Diabetes?

2021· article· en· W4200570113 on OpenAlexaff
Mohammad Fatehi, Mortaza Fatehi Hassanabad, Ali Fatehi Hassanabad

Bibliographic record

VenueCurrent Diabetes Reviews · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineType 2 diabetesDipeptidyl peptidase-4Diabetes mellitusIntensive care medicineClinical trialHeart failureLinagliptinBioinformaticsPharmacologyInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Dipeptidyl peptidase-4 inhibitors (DDP-4Is) or gliptins have been extensively studied in recent years. These studies have shown the safety and efficacy of gliptins in managing hyperglycemia in diabetic patients. However, there is an ongoing debate on whether DDP-4Is are associated with a higher risk for developing heart failure. It is expected that long-term data from patients who are currently prescribed DDP-4Is will provide a clearer understanding of their potential benefits. This should also help guide the development of future guidelines. The focus of this perspective is on associations between the "use of DPP-4Is" and "increased risk of heart failure". Thus, we examine several key publications and reviews on clinical trials on this class of oral antidiabetic medications. For this communication, the pertinent literature has been critically analyzed to provide an evidence-based overview of the evolving concept of DPP-4Is-induced risk of heart failure.

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)0.004
metaresearch head score (Gemma)0.019
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 score0.005
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.019
GPT teacher head0.274
Teacher spread0.254 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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