Piloting a Novel Algorithm for Glucose Control in the Coronary Care Unit
Bibliographic record
Abstract
OBJECTIVE: Elevated glucose levels are common after an acute myocardial infarction (AMI) and increase the risk of death. Prior trials of glucose control after AMI have been inconsistent in their ability to lower glucose levels and have reported mixed effects on mortality. We developed a paper-based glucose-lowering algorithm and assessed its feasibility and safety in the setting of AMI. RESEARCH DESIGN AND METHODS: A total of 287 participants with an acute ST segment elevation myocardial infarction (STEMI) and a capillary glucose level ≥8.0 mmol/L were randomly allocated to glucose management with intravenous glulisine insulin using this algorithm in the coronary care unit (CCU), followed by once-daily subcutaneous insulin glargine for 30 days versus standard glycemic approaches. The primary outcome was a difference in mean glucose levels at 24 h. Participants were followed for clinical outcomes through 90 days. RESULTS: At 24 h, the mean glucose level was 1.41 mmol/L (95% CI 0.69-2.13) lower in the insulin (6.53 vs. 7.94 mmol/L). Differences in glucose levels were maintained at 72 h and 30 days. A total of 22.7% of the insulin group versus 4.4% of the standard group had biochemical hypoglycemia (with neither signs nor symptoms) in the CCU because of lower glycemic goals. However, there were no differences in symptomatic hypoglycemia or clinical outcomes between the groups. CONCLUSIONS: A paper-based insulin algorithm targeting glucose levels of 5.0-6.5 mmol/L (90-117 mg/dL) can be feasibly implemented in the CCU. A cardiovascular outcomes trial using this approach can determine whether targeted glucose lowering improves patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".