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Association of sodium glucose co-transporter-2 inhibitors with risk of diabetic ketoacidosis among hospitalized patients: A multicentre cohort study

2024· article· en· W4401076542 on OpenAlexaff
Shohinee Sarma, Benazir Hodzic-Santor, Afsaneh Raissi, Michael Colacci, Amol A. Verma, Fahad Razak, Mats Christian Højbjerg Lassen, Michael Fralick

Bibliographic record

VenueJournal of Diabetes and its Complications · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsToronto General HospitalSt. Michael's HospitalUniversity of TorontoSinai Health System
Fundersnot available
KeywordsMedicineDiabetic ketoacidosisDiabetes mellitusKetoacidosisCohortInternal medicineCohort studyTransporterEndocrinologyType 1 diabetes

Abstract

fetched live from OpenAlex

Sodium glucose co-transporter-2 inhibitors (SGLT-2i) are increasingly being used among hospitalized patients. Our objective was to assess the risk of diabetic ketoacidosis (DKA) among hospitalized patients receiving an SGLT-2i. We conducted a multicentre cohort study of patients hospitalized at 19 hospitals. We included patients over 18 years of age who received an SGLT-2i or a dipeptidyl peptidase-4 inhibitor (DPP-4i) in hospital. The primary outcome was the risk of DKA during their hospitalization. 61,517 patients received a DPP-4i and 11,061 received an SGLT-2i. The risk of inpatient DKA was 0.07 % ( N = 41 events) among adults who received a DPP-4i and 0.18 % ( N = 20 events) among adults who received an SGLT-2i; adjusted odds ratio of 3.30 (95 % CI: 1.85–5.72). In hospitalized patients, the absolute risk of DKA was 0.2 %, which corresponded to a three-fold higher relative risk. • We conducted a multicentre cohort study of hospitalized adults to examine the risk of diabetic ketoacidosis (DKA) among patients who received an SGLT2 inhibitor in hospital (SGLT-2i). • The absolute risk of DKA with SGLT-2i was 0.2%, results consistent with a recent meta-analysis of SGLT-2i use in the outpatient setting.

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 score0.005
Threshold uncertainty score0.438

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 head0.003
GPT teacher head0.208
Teacher spread0.205 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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