Prevalence and outcomes of mild stroke patients undergoing reperfusion therapy: A meta-analysis and SAFE recommendations for optimal management
Bibliographic record
Abstract
Background: Mild acute ischemic stroke (AIS), characterized by a National Institutes of Health Stroke Scale (NIHSS) score of 5 or less, can lead to significant long-term disabilities. Reperfusion therapies like intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT) are commonly used in AIS, but their efficacy and safety in mild stroke cases remain unclear. Objectives: This meta-analysis aims to clarify the prevalence of mild AIS and evaluate the outcomes of reperfusion therapy, specifically IVT and EVT, in terms of functional recovery, mortality, stroke recurrence, and adverse events such as symptomatic intracerebral hemorrhage (sICH), intracerebral hemorrhage (ICH), and early neurological deterioration (END). Design: A meta-analysis was conducted following PRISMA guidelines to combine and assess the results of independent studies examining the use of reperfusion therapies in patients with mild AIS. Data Sources and Methods: A systematic search of PubMed, Embase, and Cochrane databases was performed. Studies assessing mild AIS prevalence and the outcomes of reperfusion therapy were included. Random effects modelling was applied to evaluate associations between reperfusion therapy and clinical outcomes at 90 days. Results: Fifty-six studies, including 474 778 patients, were analyzed. The pooled prevalence of mild stroke was 54% among all AIS cases, 29% in IVT-treated patients, and 9% in EVT-treated patients. Reperfusion therapy was associated with significantly increased odds of sICH (OR 2.92), ICH (OR 2.20), and END (OR 2.37). However, no significant association was found with excellent functional outcomes (OR 0.93), good functional outcomes (OR 0.91), mortality (OR 1.14), or stroke recurrence (OR 0.93) at 90 days. Variations were observed between different reperfusion subgroups. Conclusion: Mild AIS is prevalent, and reperfusion therapy in these cases is linked to higher rates of adverse events without a clear benefit in functional outcomes or mortality. These findings support the need for selective reperfusion therapy in mild stroke patients. The proposed SAFE framework-Selective use of IVT, Assessment of individual factors, Focus on EVT for large vessel occlusion (LVO), and Establishment of region-specific guidelines-may help guide clinical decisions. Further research should refine patient selection criteria and explore adjunctive therapies.
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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 | 0.035 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.064 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".