Sulfonylurea Drugs Do Not Influence Initial Stroke Severity and In-Hospital Outcome in Stroke Patients With Diabetes
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Sulfonylurea drugs inhibit ATP-dependent potassium channels and may increase mortality after myocardial infarction. Sulfonylurea drugs also inhibit ischemic preconditioning in experimental models of brain ischemia and in clinical studies in the human heart. METHODS: In the present study we examined the impact of sulfonylurea drugs on in-hospital mortality and the immediate neurological deficit of diabetic stroke patients. From a larger stroke data bank, we studied 146 diabetic patients with acute hemispheric ischemic stroke. Sixty patients were using sulfonylurea drugs. RESULTS: Major baseline characteristics such as age, blood pressure, admission glucose level, HbA(1c), distribution of cardiovascular risk factors, and presumed stroke etiology (Trial of Org 10172 in Acute Stroke Treatment [TOAST] criteria) were not different. Mortality (15% versus 14%; P=0.86) and initial stroke severity (Canadian Neurological Scale score, 7.4 versus 7.5; P=0.79) were not significantly different between patients with and without sulfonylurea drugs. Further end points such as Rankin Scale score, deteriorating stroke, duration of hospital stay, type of infarcts on CT/MRI, requirement of intensive care, and complications were not different. In a stepwise logistic regression model, sulfonylurea drugs were not independent predictors for increased mortality, deteriorating stroke, or stroke severity. CONCLUSIONS: In the present hospital-based study, sulfonylurea drugs in patients with diabetes and stroke are not associated with increased stroke severity, mortality, or a worse in-hospital outcome.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".