Twenty‐four hour hyperinsulinemic‐euglycemic clamp improves postoperative nitrogen balance only in low insulin sensitivity patients following cardiac surgery
Bibliographic record
Abstract
BACKGROUND: Critically ill patients often suffer from a protein catabolic state. The aim of this study was to demonstrate that nitrogen balance (NB) in cardiac patients admitted to the intensive care unit (ICU) is related to their insulin sensitivity level and that supraphysiologic doses of insulin can restore anabolism. MATERIALS AND METHODS: Twenty-eight patients that were admitted to ICU in enteral and/or parenteral nutrition have been enrolled in this study. All patients received a standard nutrition protocol for at least 3 days before starting the study. These patients received either enteral or parenteral nutrition based on 1.4 kcal/kg/h and 1.1 g/kg/24 h of proteins. Participants were studied for three 24 h periods (P1 , P2 , and P3 ). Twenty-four hour NB was calculated from urinary urea nitrogen excretion, fixed protein and energy intake during each of the three periods (P1 , P2 , and P3 ). Simultaneous to P2, a 24 h hyperinsulinemic-euglycemic clamp (HEC) was performed to determine patients' insulin sensitivity (IS) or insulin resistance (IR), as well as the impact of high doses of insulin on NB. RESULTS: Nitrogen balance remained consistently positive in the IS group regardless of the clamp. In IR patients, NB was negative before the clamp and became positive during P2 and P3 . Insulin sensitivity improved during the HEC in IR patients (P < 0.001). CONCLUSIONS: A negative NB was found only in insulin resistant patients admitted to the ICU for more than 7 days. A 24-h period HEC improved NB in these 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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".