Glucose and Insulin Administration While Maintaining Normoglycemia During Cardiac Surgery Using a Computer-Assisted Algorithm
Bibliographic record
Abstract
BACKGROUND: applying the principles of the hyperinsulinemic-normoglycemic clamp technique we have introduced glucose and insulin administration while maintaining normoglycemia (GIN therapy) to surgical patients. The objective of this study was to evaluate a novel computer software (GIN Computer Software [GINCS]) program using an algorithm based on the original clamp equation and modified for its use during cardiac surgery. METHODS: thirty-six patients without diabetes undergoing elective cardiac surgery were randomly assigned to manually controlled or computer-guided GIN therapy. In both groups insulin was administered at 5 mU/kg/min during surgery. Simultaneously, 20% dextrose was infused at a rate adjusted to maintain blood glucose (BG) between 4.0 and 6.0 mmol/L. The adjustments were made either following an algorithm based on our previous GIN experience or suggestions made by the software program. The primary outcome was the achievement of target glycemia. RESULTS: normoglycemia was achieved in both groups as reflected by mean BG concentrations of 5.0 ± 0.5 mmol/L and 5.1 ± 0.2 mmol/L. Mean sampling intervals were longer in the GINCS group than in the manual group (21.5 ± 1.9 vs. 14.2 ± 2.2 min, P < 0.001). The GINCS therapy was associated with a greater percentage of BG measurements within target (manual group, before cardiopulmonary bypass [CPB] 79.7%, during CPB 68.1%, and after CPB 69.1%; GINCS group, before CPB 94.1%, during CPB 92.4%, and after CPB 97.7%; P < 0.001). No hypoglycemia was observed. CONCLUSIONS: the use of a computer-guided GIN protocol in patients without diabetes undergoing open heart surgery provided excellent and safe glycemic control.
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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 | 0.003 |
| 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".