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Record W2992060748 · doi:10.1097/hco.0000000000000704

Sodium-glucose cotransporter 2 inhibitors and type 2 diabetes: clinical pearls for in-hospital initiation, in-hospital management, and postdischarge

2019· article· en· W2992060748 on OpenAlexaff
C. David Mazer, Amel Arnaout, Kim A. Connelly, Jeremy Gilbert, Stephen Glazer, Subodh Verma, Ronald Goldenberg

Bibliographic record

VenueCurrent Opinion in Cardiology · 2019
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of TorontoSt. Michael's HospitalSunnybrook Health Science CentreLMC Diabetes & Endocrinology (Canada)Humber River Regional HospitalQueen's UniversityHealth Sciences CentreOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineIntensive care medicineType 2 diabetesCotransporterDiabetes mellitusAdverse effectInternal medicineSodiumEndocrinology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The aim of this article is to provide practical recommendations on safe initiation of sodium-glucose cotransporter 2 (SGLT2) inhibitors to in-patients as well as management of those who are already on SGLT2 inhibitors. RECENT FINDINGS: Robust data from stable outpatient cohorts indicate that the SGLT2 inhibitors are associated with clinically meaningful reductions in major adverse cardiovascular events, lower rates of hospitalization for heart failure, and a reduction in major kidney outcomes There is however a lack of information on how to initiate and manage SGLT2 inhibitors in an acute in-patient setting. SUMMARY: SGLT2 inhibitors may be cautiously appropriate for in-patients if all the criteria for safe use are met but good clinical judgment must prevail. Temporary withholding of SGLT2 inhibitors is appropriate in hospitalized patients during a period of stress and/or insulinopenia.

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)0.005
metaresearch head score (Gemma)0.018
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: Editorial · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.323
Teacher spread0.298 · 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
GenreEditorial

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

Citations11
Published2019
Admission routes1
Has abstractyes

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