Predictors of Recurrent Stroke After Embolic Stroke of Undetermined Source in the RE‐SPECT ESUS Trial
Bibliographic record
Abstract
Background We sought to determine recurrent stroke predictors among patients with embolic strokes of undetermined source (ESUS). Methods and Results We applied Cox proportional hazards models to identify clinical features associated with recurrent stroke among participants enrolled in RE‐SPECT ESUS (Randomized, Double‐Blind, Evaluation in Secondary Stroke Prevention Comparing the Efficacy and Safety of the Oral Thrombin Inhibitor Dabigatran Etexilate Versus Acetylsalicylic Acid in Patients With Embolic Stroke of Undetermined Source) trial, an international clinical trial evaluating dabigatran versus aspirin for patients with ESUS. During a median follow‐up of 19 months, 384 of 5390 participants had recurrent stroke (annual rate, 4.5%). Multivariable models revealed that stroke or transient ischemic attack before the index event (hazard ratio [HR], 2.27 [95% CI, 1.83–2.82]), creatinine clearance <50 mL/min (HR, 1.69 [95% CI, 1.23–2.32]), male sex (HR, 1.60 [95% CI, 1.27–2.02]), and CHA 2 DS 2 ‐VASc ≥4 (HR, 1.55 [95% CI, 1.15–2.08] and HR, 1.66 [95% CI, 1.21–2.26] for scores of 4 and ≥5, respectively) versus CHA 2 DS 2 ‐VASc of 2 to 3, were independent predictors for recurrent stroke. Conclusions In RE‐SPECT ESUS trial, expected risk factors previously linked to other common stroke causes were associated with stroke recurrence. These data help define high‐risk groups for subsequent stroke that may be useful for clinicians and for researchers designing trials among patients with ESUS. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02239120.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".