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Record W4405836136 · doi:10.1016/j.cjco.2024.12.008

Sodium Glucose Cotransporter Inhibition in Acute Heart Failure: An In-Depth Review

2024· review· en· W4405836136 on OpenAlexaff
Joey Mercier, Aditya Sharma, Magdaline Zawadka, Thang Nguyen

Bibliographic record

VenueCJC Open · 2024
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSt. Boniface HospitalUniversity of Manitoba
Fundersnot available
KeywordsTransporterHeart failureGlucose transporterSodiumInternal medicineCardiologyMedicineChemistryBiochemistryInsulin

Abstract

fetched live from OpenAlex

Inhibitors of the sodium glucose cotransporters were initially developed for the treatment of type 2 diabetes but have since been shown to provide many benefits in heart failure, especially in heart failure with reduced ejection fraction (HFrEF). Already an established and foundational therapy for HFrEF, many uncertainties remain with respect to the use and initiation of SGLT2 inhibitors in hospitalized settings and in sicker individuals. Unfortunately, clear guidance or guidelines on this topic is lacking, but over the past few years, several trials have come out attempting to answer this question. This in-depth review aims at summarizing the current evidence not only as it pertains to the use of SGLT2 inhibitors in acute decompensated heart failure but also during acute myocardial infarction. From a brief examination of the history of SGLT2 inhibitor development to an appraisal on where along the spectrum of heart failure SGTL2 inhibition initiation should be considered, this review will also focus on potential advantages of starting SGLT2 inhibition in hospitals, the likely "sweet spot" in terms of timing of initiation, the diuretic augmentation effects of SGLT2 inhibition and how it compares with more traditional sequential blockade with thiazides, and, finally, an in-depth review of the safety surrounding the use of SGLT2 inhibitors in hospitalized patients.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.762
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.046
GPT teacher head0.367
Teacher spread0.321 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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