Insulin Resistance and Hyperglycemia in Critical Illness
Bibliographic record
Abstract
Alterations in glucose metabolism, including hyperglycemia associated with insulin resistance, occur in critical illness. Acutely, such alterations result from normal, adaptive activation of endocrine responses, including increased release of catecholamines, cortisol, and glucagon and a reduced glucose uptake capacity. In prolonged critical illness, neuroendocrine changes lead to more extensive metabolic changes that may be associated with development of complications and poor prognosis. Until recently, hyperglycemia was not routinely controlled in intensive care units, except among patients with known diabetes mellitus. Studies have demonstrated that glycemic management in postmyocardial infarction in patients with diabetes is an effective practice. Recent investigation has extended this to demonstrate reduced morbidity and mortality in a surgical critically ill population with and without diabetes mellitus in later phases of critical illness. Although the mechanisms for improved patient outcomes need to be established, this novel approach to management of hyperglycemia in critical illness is a new and important concept for those working in critical care. This article reviews alterations in glucose metabolism which occur in critically ill patients and discusses potential mechanisms and mediators (e.g., hormones, cytokines) that may play a key role in hyperglycemia and insulin resistance during acute and prolonged phases of severe illness. The article addresses the application of insulin protocols and exogenous regulation of glucose concentration in critical illness based on a review of recent intervention studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".