Prognostic Value of Serum D-Dimer in Noncardioembolic Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Although D-dimer levels are significantly associated with cardioembolic infarction, the significance of D-dimer levels in relation to the severity and functional outcomes of other stroke subtypes, such as lacunar and large artery atherosclerosis infarction, remains unclear. The purpose of this study was to evaluate whether elevated initial D-dimer levels are significantly and cross-sectionally associated with poor functional outcomes at each time point during a 9-month follow-up period. We also investigated the significance of D-dimer levels in longitudinal temporal changes of functional outcomes in these patients. METHODS: We recruited 146 patients with lacunar infarction and 161 patients with large artery atherosclerosis infarction who were consecutively admitted to our hospital after acute stroke. Serum D-dimer levels were evaluated initially and the modified Rankin scale were measured initially and at 1-, 3-, 6-, and 9-month follow-up visits. RESULTS: Patients with higher D-dimer levels had significantly worse initial functional outcomes, and these worse outcomes were maintained throughout the 9-month follow-up period compared with the low D-dimer group. However, regardless of stroke subtype, D-dimer levels did not influence long-term changes in functional outcomes over the 9-month follow-up period. CONCLUSION: This study suggests that elevated D-dimer levels can be used as a surrogate marker for poor functional outcomes only during the acute stage. Further evaluation of serum D-dimer levels could provide a helpful predictive marker for stroke prognosis.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".