The emerging value of serum D-dimer measurement in the work-up and management of ischemic stroke
Bibliographic record
Abstract
BACKGROUND: Elevated D-dimer levels are a marker of both thrombin formation and fibrinolysis. Currently D-dimer measurement is routinely used for ruling out venous thromboembolism and diagnosis/monitoring of disseminated intravascular coagulation. Recent emerging data suggest that D-dimer may become an important biomarker in ischemic stroke as well as in cardiovascular diseases. AIMS: To outline the clinical utility of D-dimer in work-up and management of ischemic stroke. SUMMARY: D-dimer measurement is most useful in stroke with active cancer as it can confirm etiologic diagnosis, predict recurrent stroke risk, and aid treatment decision in cancer-associated stroke. In cryptogenic stroke, high D-dimer levels can also provide clues for the cause of stroke as occult cancer and undetected cardiac embolic source as occult atrial fibrillation and may be helpful in treatment decision making of secondary stroke prevention. Serial D-dimer measurements should be further studied to monitor antithrombotic therapy effectiveness in both cardiogenic and cryptogenic etiologies. CONCLUSION: Accumulating data suggests the utility of D-dimer test in the management of ischemic stroke, although the evidence is still limited. Future studies would clarify the role of D-dimer measurement in ischemic stroke.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".