Nurse- vs Nomogram-Directed Glucose Control in a Cardiovascular Intensive Care Unit
Bibliographic record
Abstract
BACKGROUND: Paper-based nomograms are reasonably effective for achieving glycemic control but have low adherence and are less adaptive than nurses' judgment. OBJECTIVE: To compare efficacy (glucose control) and safety (hypoglycemia) achieved by use of a paper nomogram versus nurses' judgment. METHODS: Prospective, randomized, open-label, crossover trial in an intensive care unit in postoperative patients with glucose concentrations greater than 8 mmol/L. Consenting nurses with at least 1 year of experience were randomized to use either their judgment or a validated paper-based nomogram for glucose control. After completion of 2 study shifts, the nurses used the alternative method for the next 2 study shifts. Glucose target level and safety and efficacy boundaries were the same for both methods. The primary end point was area under glucose time curve per hour. RESULTS: Thirty-four nurses contributed 95 shifts of data (44 nomogram-directed, 51 nurse-directed). Adherence to the nomogram was higher in the nomogram group than hypothetical adherence in the nurse-directed group for correct adjustments in insulin infusion (70% vs 37%; P < .001) and glucose checks (58% vs 43%; P = .008). The primary end point did not differ between the 2 groups (mean, 9.0 mmol/L; SD, 3.5 vs mean, 8.3 mmol/L; SD, 2.1; P = .08). Glucose variability, amount of time patients were hypoglycemic or hyperglycemic, and number of glucose checks performed were similar in the 2 groups. CONCLUSIONS: In an intensive care unit where nurses generally accepted the need for tight glucose control, nurse-directed control was as effective and as safe as nomogram-based 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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".