Atrial fibrillation detected after stroke is related to a low risk of ischemic stroke recurrence
Bibliographic record
Abstract
<h3>Objective</h3> To compare the risk of 1-year ischemic stroke recurrence between atrial fibrillation (AF) diagnosed after stroke (AFDAS) and sinus rhythm (SR) and investigate whether underlying heart disease is as frequent in AFDAS as it is in AF known before stroke (KAF). <h3>Methods</h3> In this retrospective cohort study, we included all ischemic stroke patients admitted to institutions participating in the Ontario Stroke Registry from July 1, 2003, to March 31, 2013. Based on heart rhythm assessed during admission, we classified patients as AFDAS, KAF, or SR. We modeled the relationship between heart rhythm groups and 1-year ischemic stroke recurrence by using Cox regression adjusted for multiple covariates (e.g., oral anticoagulants). We compared the prevalence of coronary artery disease, myocardial infarction, and heart failure among the 3 groups. <h3>Results</h3> Among 23,376 ischemic stroke patients, 15,885 had SR, 587 AFDAS, and 6,904 KAF. At 1 year, 39 (6.6%) patients with AFDAS, 661 (9.6%) with KAF, and 1,269 (8.0%) with SR had recurrent ischemic strokes (<i>p</i> = 0.0001). AFDAS-related ischemic stroke recurrence adjusted risk was not different from that of SR (hazard ratio 0.90 [95% confidence interval 0.63, 1.30]; <i>p</i> = 0.57). Prevalence of coronary artery disease (18.2% vs 34.7%; <i>p</i> < 0.0001), myocardial infarction (11.6% vs 20.5%; <i>p</i> < 0.0001), and heart failure (5.5% vs 16.8%; <i>p</i> < 0.0001) were lower in AFDAS relative to KAF. <h3>Conclusions</h3> The lack of difference in 1-year ischemic stroke recurrence between AFDAS and SR and the lower prevalence of heart disease in AFDAS compared to KAF suggest that the underlying pathophysiology of AFDAS may differ from that of KAF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".