Metabolic effects of intensive insulin therapy in critically ill patients
Bibliographic record
Abstract
Our aim was to investigate the effects of glycemic control and insulin concentration on lipolysis, glucose, and protein metabolism in critically ill medical patients. For our methods, the patients were studied twice. In study 1, blood glucose (BG) concentrations were maintained between 7 and 9 mmol/l with intravenous insulin. After study 1, patients entered one of four protocols for 48 h until study 2: low-insulin high-glucose (LIHG; variable insulin, BG of 7-9 mmol/l), low-insulin low-glucose (LILG; variable insulin of BG 4-6 mmol/l), high-insulin high-glucose [HIHG; insulin (2.0 mU . kg(-1).min(-1) plus insulin requirement from study 1), BG of 7-9 mmol/l], or high-insulin low-glucose [HILG; insulin (2.0 mU.kg(-1).min(-1) plus insulin requirement from study 1), BG of 4-6 mmol/l]. Age-matched healthy control subjects received two-step euglycemic hyperinsulinemic clamps achieving insulin levels similar to the LI and HI groups. In our results, whole body proteolysis was higher in patients in study 1 (P < 0.006) compared with control subjects at comparable insulin concentrations and was reduced with LI (P < 0.01) and HI (P = 0.001) in control subjects but not in patients. Endogenous glucose production rate (R(a)), glucose disposal, and lipolysis were not different in all patients in study 1 compared with control subjects at comparable insulin concentrations. Glucose R(a) and lipolysis did not change in any of the study 2 patient groups. HI increased glucose disposal in the patients (HIHG, P = 0.001; HILG, P = 0.07 vs. study 1), but this was less than in controls receiving HI (P < 0.03). In conclusion, low-dose intravenous insulin administered to maintain BG between 7-9 mmol/l is sufficient to limit lipolysis and endogenous glucose R(a) and increase glucose R(d). Neither hyperinsulinemia nor normoglycemia had any protein-sparing effect.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".