Role of D-Dimer in Stroke: A Systematic Review
Bibliographic record
Abstract
Introduction and Objective: cute cerebral strokes lead to complex chronic disabilities worldwide, bearing high morbidity and mortality. Around 7 in 10 strokes occur in low- and middle-income countries (LMIC). D-dimer is a commonly performed laboratory test that is easily accessible in LMIC. This systematic review aims to evaluate the effectiveness of D-dimer as a diagnostic predictor of stroke within the 6- and 24-hour time period. Methods: This systematic review adhered to PRISMA guidelines. Keywords including stroke, D-dimer, laboratory testing, and indicators were used. PubMed, Scopus, and CINAHL Plus were searched. Quality appraisal was conducted using the Newcastle Ottawa Scale. Results: A total of nine studies were included in the review. Studies were conducted in Spain (n=3), Germany (n=1), China (n=1), Turkey (n=1), USA (n=1), Korea (n=1), and Italy (n=1). Statistical significance of D-dimer values was found in 6 of the nine studies (66.6%). The overall quality of evidence is considered to be at the upper-moderate level. Conclusion: D-dimer is a promising biomarker that may be utilized and fully scaled as a rapid biochemical test to diagnose stroke. As the lab test is already conducted across many healthcare settings, the extension to testing in patients with acute cerebrovascular ischemic events will help predict the exact stroke type and quicken treatment formalities. This systematic review identifies statistically signi-ficant (P<0.05) differences of plasma D-dimer values within 6 and 24 hours among stroke and stroke-mimicking patients. No optimal cut-off value was determined due to the dearth of data. An optimal cut-off value of plasma D-dimer levels must be determined in future clinical studies to estimate the sensitivity and specificity of D-dimer in diagnosing 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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".