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Record W4415184969 · doi:10.4239/wjd.v16.i10.111212

Transient extreme insulin resistance in a patient requiring extracorporeal membrane oxygenation for cardiogenic shock: A case report

2025· article· en· W4415184969 on OpenAlexaff
Jean‐François Légaré, Christopher W. White

Bibliographic record

VenueWorld Journal of Diabetes · 2025
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsCanada East Spine CentreUniversity of New BrunswickSaint John Regional HospitalDalhousie University
Fundersnot available
KeywordsExtracorporeal membrane oxygenationPerfusionDiabetes mellitusVasoactiveInsulin resistanceCardiogenic shockVascular resistance

Abstract

fetched live from OpenAlex

BACKGROUND: Acute and extreme insulin resistance with persistent hyperglycemia requiring excessively high doses of insulin before rapidly resolving is rare and has been referred to as transient and extreme insulin resistance (TEIR). The underlying pathophysiology and optimal management of TEIR are poorly understood, and previous reports of TEIR in the literature are sparse. This report is the first description of TEIR in a patient requiring mechanical circulatory support (MCS). CASE SUMMARY: A 62-year-old male developed cardiogenic shock and was placed on veno-arterial extracorporeal membrane oxygenation following percutaneous coronary intervention and successful revascularization. Over the next 24 hours, glucose levels rose and remained markedly elevated despite increasing insulin infusion rates and repeated boluses. The insulin infusion rate peaked at 450 units/hour, and the patient received 4300 units (33 units/kg) of insulin over the 24-hour period of peak insulin resistance. Insulin resistance resolved rapidly, necessitating an abrupt decrease in the insulin infusion rate and development of rebound hypoglycemia. CONCLUSION: Onset of TEIR did not seem to correlate with end-organ hypoperfusion or vasoactive drug dosing.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.231
Teacher spread0.215 · 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 designBench or experimental
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

Citations1
Published2025
Admission routes1
Has abstractyes

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