Predictors of Stroke Recurrence After Initial Diagnosis of Cervical Artery Dissection
Bibliographic record
Abstract
Abstract Objective Patients with cervical artery dissection (CAD) are at increased ischemic stroke risk. We aimed to identify characteristics that are associated with increased risk of ischemic stroke following initial presentation of CAD and to evaluate the differential impact of anticoagulant versus antiplatelet therapy in these high-risk individuals. Methods This was a secondary analysis of the Antithrombotic Treatment for Stroke Prevention in Cervical Artery Dissection (STOP-CAD) study, a multicenter retrospective observational study. The primary outcome was subsequent ischemic stroke by day 180 after diagnosis. Patient characteristics were compared between those with vs. without subsequent ischemic stroke. Significant predictors were identified using stepwise Cox regression. Associations between subsequent ischemic stroke risk and antithrombotic therapy type in high-risk patients were explored using adjusted Cox regression. Results 4,023 patients (mean age 47.4 years; 44.5% were women) were included. By day 180, 5.3% experienced a subsequent ischemic stroke. In adjusted Cox regression, factors associated with increased subsequent ischemic stroke risk were prior ischemic stroke (aHR 7.31, 95% CI 1.61-33.13, p=0.010), presentation within seven days from symptoms, (aHR 3.04, 95% CI 1.04-8.91, p=0.043), infarct on imaging (aHR 9.85, 95% CI 3.65-26.58, p<0.001), and occlusive dissection (aHR 2.34, 95% CI 1.03-5.34, p=0.043). Only patients with occlusive dissection had reduced subsequent ischemic stroke risk with anticoagulation versus antiplatelets (HR 0.37; 95% CI 0.15-0.89, p=0.03). Interpretation This study identified several predictors of subsequent ischemic stroke among patients with CAD but only patients with occlusive dissection demonstrated a benefit from anticoagulation. These findings require validation by meta-analyses of prior studies.
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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 | 0.000 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".