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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".