Atrial cardiopathy in patients with embolic strokes of unknown source and other stroke etiologies
Bibliographic record
Abstract
Objective To investigate the prevalence and clinical determinants of atrial cardiopathy in patients with embolic stroke of unknown source (ESUS) and compare with other established stroke etiologies. Methods In a cross-sectional study of 846 consecutive patients with ischemic stroke, we compared the prevalence of atrial cardiopathy (defined by p-wave terminal force in V1 >5,000 µV·ms or severe left atrial enlargement) between ESUS patients and patients with large artery atherosclerosis (LAA) and small vessel disease (SVD) strokes. Baseline characteristics were also compared between ESUS and cardioembolic (CE) patients. Results Of all, 158 (19%) patients met ESUS diagnostic criteria, while others were classified into LAA (n = 224, 26%), SVD (n = 154, 18%), and CE (n = 310, 37%). The prevalence of atrial cardiopathy was higher in ESUS patients compared to noncardioembolic stroke patients (26.6% vs 12.1% in LAA vs 16.9% in SVD; p = 0.001). ESUS patients were younger, were less hypertensive, and had higher cholesterol and low-density lipoprotein levels, but also had less left ventricular or atrial abnormalities when compared to CE patients. Conclusion The prevalence of atrial cardiopathy was high in ESUS patients compared with patients with nonembolic strokes. Interestingly, ESUS patients were also clinically different from CE patients. While the presence of atrial cardiopathy may reflect a unique mechanism of thromboembolism in ESUS patients, it is still unclear if they may benefit from anticoagulation, or if the presence of atrial cardiopathy in this population could serve as a risk-stratifying marker for stroke recurrence. Further efforts are necessary to provide better characterization of the ESUS population in order to develop better stroke preventive strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".