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Record W4406919465 · doi:10.1080/03007995.2025.2458538

Perioperative and periprocedural management of GLP-1 receptor-based agonists and SGLT2 inhibitors: narrative review and the STOP-GAP and STOP DKA-2 algorithms

2025· review· en· W4406919465 on OpenAlexafffund
Ronald Goldenberg, Jeremy Gilbert, Robyn L. Houlden, Tayyab Khan, Sapna Makhija, C. David Mazer, Jill Trinacty, Subodh Verma

Bibliographic record

VenueCurrent Medical Research and Opinion · 2025
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsBruyèreQueensway-Carleton HospitalSt. Joseph’s Healthcare HamiltonSt. Michael's HospitalWestern UniversityLMC Diabetes & Endocrinology (Canada)Queen's UniversityHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNovo Nordisk CanadaEli Lilly Canada
KeywordsMedicineNarrative reviewPerioperativeGlucagon-like peptide 1 receptorIntensive care medicineReceptorAgonistInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

The GLP-1 receptor-based agonists (GLP-1RAs) and SGLT2 inhibitors (SGLT2i) are major twenty first century breakthroughs in diabetes and obesity medicine but there are important safety considerations regarding the perioperative and periprocedural management of individuals who are treated with these agents. GLP-1RAs have been linked to an increased risk of retained gastric contents and pulmonary aspiration while SGLT2i can be associated with diabetic ketoacidosis. This manuscript provides a narrative review of the available evidence for perioperative and periprocedural risks in people prescribed GLP-1RAs and SGLT2i. The authors provide expert opinion-driven recommendations and algorithms on how to safely manage GLP-1RAs and SGLT2i under perioperative/periprocedural settings.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.834
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.097
GPT teacher head0.469
Teacher spread0.372 · 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 designOther design
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

Citations10
Published2025
Admission routes2
Has abstractyes

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