The efficacy and safety of glucose control algorithms in intensive care: a pilot study of the Survival Using Glucose Algorithm Regulation (SUGAR) trial
Bibliographic record
Abstract
INTRODUCTION: The benefits, harms and feasibility of intensive insulin therapy in critically ill patients remain unclear. Several single center studies have attempted to demonstrate the benefit of intensive insulin therapy in critically ill patients with variable results. OBJECTIVES: We conducted a pilot randomized trial to assess the feasibility, safety and clinical outcomes of preprinted glucose management algorithms before the initiation of a large multicenter trial. PATIENTS AND METHODS: Within 48 hours of admission to the intensive care unit, we randomized mechanically ventilated patients to either the "high" group (target serum glucose concentration 9-11 mmol/l) or the "low" group (target serum glucose concentration 5-7 mmol/l). To assess feasibility we measured the time to reach target glucose range, time in target range, morning glucose concentrations, average daily glucose concentrations, and number of crossovers. To assess safety, we measured the number of hypoglycemic events (serum glucose <2.2 mmol/l), and other serious adverse events such as cardiac arrests and seizures. RESULTS: Sixty-eight patients were enrolled (35 in the high group and 33 in the low group). During the first week, the median proportions of time spent in the target range were 35.7% and 53.0% for the high and low groups, respectlively (p = 0.0001). Morning glucose concentrations were 8.3 +/-1.6 mmol/l and 6.2 -/+1.2 mmol/l. One (2.9%) and 8 (24.2%) episodes of hypoglycemia (<2.2 mmol/l) occurred in the high and low groups, reflecting 0.002 and 0.03 hypoglycemic events per patient-day, respectlively. CONCLUSIONS: This pilot trial of intensive insulin therapy identified numerous challenges that helped in the preparation of an international multicenter randomized trial of intensive insulin therapy to evaluate benefits and harms.
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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 | 0.008 | 0.007 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".