D-Dimer Levels as a Predictor of Clinical Outcome and Mortality in Acute Ischemic Stroke Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Highlight: An increase in the D-dimer level indicates activation of the coagulation system through thrombus formation and fibrinolysis. The meta-analysis found a significant relationship between elevated d-dimer levels and worsening clinical outcomes and increased mortality. The D-dimer level can be used as a predictor for predicting clinical outcomes and mortality in acute ischemic stroke patients at each duration of follow-up. ABSTRACT Introduction: In ischemic stroke, high D-dimer levels are frequently found, indicating coagulation with ongoing thrombus formation and fibrinolysis. Objective: The purpose of this study was to analyze the role of D-dimer in predicting clinical outcomes and mortality in acute ischemic stroke patients. Methods: A systematic literature search was conducted using the PRISMA method through the PubMed, Science Direct, and Google Scholar databases. The quality of the article was assessed using the Newcastle-Ottawa Scale (NOS) and statistically analyzed using Review Manager software version 5.4.1. Results: Eight articles had good quality according to NOS and matched the criteria for the literature search. Elevated D-dimer levels and worsened clinical outcomes have a significant result when discharged from the hospital: OR 2.37 (95% CI 1.68–3.35); I2 = 45% p < 0.00001; 1-month: OR 1.75 (95% CI 1.38–2.23), I2 = 47% p < 0.00001; 3-months: OR 2.43 (95% CI 2.00–2.95), I2 0% p < 0.00001; 6-months: OR 2.64 (95% CI 1.92–3.63), I2 = 0% p < 0.00001; and 12-months: OR 1.92 (95% CI 1.31–2.82), I2 = 62% p < 0.0008. Elevated D-dimer level and increased mortality have a significant result with OR 2.25 (95% CI 1.78–2.85), I2 = 45% p < 0.00001. Conclusion: D-dimer can be used as a predictor of clinical outcome and mortality in acute ischemic stroke.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| 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".