Intravenous insulin nomogram improves blood glucose control in the critically ill
Bibliographic record
Abstract
OBJECTIVE: To improve control of blood glucose concentrations in critically ill patients through use of a bedside, nurse-managed, intravenous insulin nomogram. DESIGN: Retrospective, before-after cohort study. SETTING: Fifteen-bed mixed medical/surgical intensive care unit in a tertiary, teaching hospital. PATIENTS: A total of 167 intensive care unit patients requiring intravenous insulin infusions during two 9-month periods. INTERVENTION: The sliding scale group was treated using ad hoc sliding scale infusion therapy. The intervention group was treated using a dosing nomogram that allowed the nurse to adjust the insulin infusion rate based on current glucose concentration and concurrent insulin infusion rates. The adjustments were made independent of physician input. MEASUREMENTS AND MAIN RESULTS: Time from initiating the insulin infusion to initial control of glucose concentration (<11.5 mmol/L) was determined. Effectiveness of glucose control was determined retrospectively by measuring the area under the curve of blood concentrations >11.5 mmol/L versus time of insulin infusion, divided by total duration of insulin infusion. The median time to initial control of glucose (<11.5 mmol/L) was 4 hr (range 1-38 hr) for the baseline and 2 hr (range 1-22 hr) for nomogram group (p =.0004). The median area under the curve of glucose concentration divided by duration of insulin infusion was 0.9 (range 0.0-5.9) for sliding scale group and 0.3 (range 0.0-11.1) for nomogram (p =.0001), without any increase in the frequency of episodes of hypoglycemia. CONCLUSION: Use of an insulin nomogram in critically ill patients improves control of blood glucose concentrations and is safe.
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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) | 0.000 | 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".