Insulin Degludec in Critically Ill Patients With Type 2 Diabetes Mellitus: A Prospective Interventional Study
Bibliographic record
Abstract
OBJECTIVE: Dysglycemia has deleterious outcomes on critically ill patients with diabetes mellitus (DM). Insulin degludec, an ultralong-acting insulin, is associated with lower rates of hypoglycemia and blood glucose (BG) variability in non-critically ill patients. The experience with insulin degludec in the intensive care units is lacking. This study aimed to assess the effect of insulin degludec on glycemic control in critically ill patients with type 2 DM. METHODS: A prospective, interventional study enrolled critically ill patients with type 2 DM. Subjects were started on insulin degludec plus insulin regular correctional doses. BG levels were assessed every 6 hours. The primary outcome was the percentage of BG levels within a target of 140 to 180 mg/dL. The secondary outcomes included the median BG levels, severe hypoglycemia rate, and BG variability. RESULTS: In total, 155 patients were enrolled. The percentage of BG levels within the target was 28.5%. The first day that the median of BG levels within target was on day 2 of insulin degludec therapy, which continued to be within the target for 1 week. Severe hypoglycemia developed in 5 patients (3.2%). The BG variability in the study was 26% using the coefficient of variation. CONCLUSION: In critically ill patients with type 2 DM, one-fourth of BG levels were within the glycemic target (140-180 mg/dL) with insulin degludec plus insulin regular correctional doses. The median BG levels were in target starting the second day of insulin degludec therapy. The favorable BG variability using insulin degludec merits further investigation for effect on clinical outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".