Diabetes: Chronic Metformin Treatment and Outcome Following Acute Stroke
Bibliographic record
Abstract
Aim To evaluate if in patients with known diabetes, pretreatment metformin will lead to less severe stroke, better outcome, and lower mortality following acute stroke. Methods The Qatar stroke database was interrogated for stroke severity and outcome in patients with ischemic stroke. Outcome was compared in nondiabetic vs. diabetic patients and in diabetic patients on metformin vs. other hypoglycemic agents. The National Institute of Health Stroke Scale was used to measure stroke severity and 90-day modified Rankin scale (mRS) score to determine outcome following acute stroke. Results In total, 4,897 acute stroke patients [nondiabetic: 2,740 (56%) and diabetic: 2,157 (44%)] were evaluated. There were no significant differences in age, risk factors, stroke severity and type, or thrombolysis between the two groups. At 90 days, mRS (shift analysis) showed significantly poor outcome in diabetic patients ( p < 0.001) but no differences in mortality. In the diabetic group, 1,132 patients were on metformin and 1,025 on other hypoglycemic agents. mRS shift analysis showed a significantly better outcome in metformin-treated patients ( p < 0.001) and lower mortality (8.1 vs. 4.6% p < 0.001). Multivariate negative binomial analyses showed that the presence of diabetes negatively affected the outcome (90-day mRS) by factor 0.17 (incidence risk ratio, IRR, 1.17; CI [1.08–1.26]; p < 0.001) when all independent variables were held constant. In diabetic patients, pre-stroke treatment with metformin improved the outcome (90-day mRS) by factor 0.14 (IRR 0.86 [CI 0.75–0.97] p = 0.006). Conclusion Similar to previous reports, our study shows that diabetes adversely affects stroke outcome. The use of prior metformin is associated with better outcome in patients with ischemic stroke and results in lower mortality. The positive effects of metformin require further research to better understand its mechanism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".