Role of microRNA in the risk stratification of ischemic strokes
Bibliographic record
Abstract
Background: Ischemic stroke is a major cause of death and morbidity, and risk classification is essential for predicting therapeutic outcomes. MicroRNAs may be useful indicators for risk stratification, as they control gene expression and influence physiological and pathological processes. Methodology: A systematic strategy was developed to search relevant material using databases like PubMed, Scopus, and Web of Science. Selection criteria included human research, a certain date, or categories of studies. Data extraction, synthesis, and analysis were carried out to find trends, similarities, and differences among the chosen studies. The study's design, sample size, methodology, statistical analysis, and any potential biases or restrictions from the selected reference papers were also taken into account. Results and findings: MicroRNA is an important biomarker for risk stratification in Ischemic Strokes. It can be used to identify Stroke-Specific microRNA Signatures, identify diagnostic and prognostic values, and regulate Vascular Inflammation, Endothelial Dysfunction, and Thrombus Formation and Resolution. It also has potential therapeutic applications. Conclusion: MicroRNAs have emerged as promising biomarkers for predicting stroke risk, severity of strokes, and clinical outcomes. They can be used to predict the severity of a stroke and aid clinicians in making treatment decisions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".