Prevalence, Incidence, and Clinical Resolution of Insulin Resistance in Critically Ill Patients: An Observational Study
Bibliographic record
Abstract
BACKGROUND: The primary objective of this study was to measure the prevalence, incidence, and resolution of insulin resistance (IR) in critically ill patients. A secondary objective was to explore the relationship between IR and inflammatory cytokines, coagulation abnormalities, and clinical outcomes. DESIGN: Prospective observational study. METHODS: The setting was the medical/surgical intensive care unit (ICU). We enrolled consecutive patients within 24 hours of admission to the ICU. Blood samples were collected daily until discharge, death, or a maximum of 10 days, then sent for measurement of markers of IR, inflammation, and coagulation. Charts were reviewed retrospectively to determine clinical outcomes. The homeostasis model assessment method (HOMA) was used to determine IR; a score of > or = 4 represents insulin resistance. RESULTS: A total of 96 patients were enrolled. Upon admission, 64 (67%) patients had overt IR (glucose > 7 mmol/L or insulin use), 9 (9.4%) had non-overt IR (normal glucose but HOMA > 4), and 23 (24%) were insulin sensitive (IS; normal glucose and HOMA < 4). During the course of ICU stay, an additional 16 patients developed overt IR, while 10 (10%) remained IS. There were no significant differences in inflammatory markers, coagulation tests, and clinical outcomes between IR and IS patients. There was no significant correlation between HOMA and inflammatory markers and coagulation markers. In a multivariable regression model, only interleukin-6 levels were significantly associated with mortality. CONCLUSIONS: A high proportion of critically ill patients have IR. There may not be any significant relationship between IR and measures of inflammation, coagulation, and clinical outcomes in a heterogeneous population of critically ill patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".