Viral Infection and Ischemic Stroke: Emerging Trends and Mechanistic Insights
Bibliographic record
Abstract
Population studies have suggested that viral infections may be contributing to risk of ischemic stroke, although the mechanisms for this are unclear. In this review, we examine the epidemiological evidence supporting the involvement of viral diseases, including influenza, COVID-19, chronic herpesvirus infections, and hepatitis C in current trends of stroke incidence. To support these associations, we highlight the virus-host interactions that are critical in the context of stroke, including direct effects of acute and persistent viral infections on vascular function, inflammation, and thrombosis. Additionally, we evaluate the systemic changes that occur during viral infection that can predispose individuals to ischemic stroke, including alterations in blood pressure regulation, coagulation, and lipid metabolism. Our review emphasizes the need to further elucidate precise mechanisms involved in viral infections and stroke risk. Future research will inform the development of targeted interventions for stroke prevention in the context of viral diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".