Impact of Prior Metformin Use on Stroke Outcomes: A Systematic Review and Updated Meta-Analysis
Bibliographic record
Abstract
Background: Metformin is a commonly prescribed oral hypoglycemic agent for diabetic patients. Its effect in reducing the incidence of stroke has already been proven. We aimed to explore the impact of prior metformin use on stroke outcomes. Methods: The Web of Science, PubMed, Embase, and Cochrane Library were searched to identify relevant studies involving stroke patients with a history of metformin use and comparing them to non-metformin users. We analyzed the following outcomes: modified Rankin Scale (mRS), National Institutes of Health Stroke Scale (NIHSS), mortality, or length of hospitalization. Results: Eleven studies, with 13,825 participants, were included. The metformin group showed higher favorable mRS 0 - 2 than the non-metformin group (risk ratio (RR) = 1.14, 95% confidence interval (CI): 1.09 - 1.19, P value < 0.01). Also, significantly lower mortality rates were seen in the metformin group (RR = 0.54, 95% CI: 0.46 - 0.63, P value ≤ 0.01). NIHSS at discharge was lower in the metformin group than the non-metformin group (mean difference (MD) = -0.46, 95% CI: -0.82 - -0.11, P value < 0.01). The mRS 3 - 6 indicates less favorable outcomes were higher in the non-metformin group (RR = 0.85, 95% CI: 0.77 - 0.93). At the same time, NIHSS at admission showed no statistically significant difference between the two groups. These results indicate that metformin has a beneficial impact on the severity of stroke. Conclusions: Pre-stroke metformin therapy is associated with better post-stroke clinical outcomes and lower mortality rates. These results highlight the potential neuroprotective role of metformin and emphasize its role as an adjunctive treatment in stroke management. Further research is required to understand its mechanism better.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".