Atrial Fibrillation Detected After Stroke and Transient Ischemic Attack: A Novel Clinical Concept Challenging Current Views
Bibliographic record
Abstract
Atrial fibrillation (AF) can be newly detected in approximately one-fourth of patients with ischemic stroke and transient ischemic attack without previously recognized AF. We present updated evidence supporting that AF detected after stroke or transient ischemic attack (AFDAS) may be a distinct clinical entity from AF known before stroke occurrence (known atrial fibrillation). Data suggest that AFDAS can arise from the interplay of cardiogenic and neurogenic forces. The embolic risk of AFDAS can be understood as a gradient defined by the prevalence of vascular comorbidities, the burden of AF, neurogenic autonomic changes, and the severity of atrial cardiopathy. The balance of existing data indicates that AFDAS has a lower prevalence of cardiovascular comorbidities, a lower degree of cardiac abnormalities than known atrial fibrillation, a high proportion (52%) of very brief (<30 seconds) AF paroxysms, and is more frequently associated with insular brain infarction. These distinctive features of AFDAS may explain its recently observed lower associated risk of stroke than known atrial fibrillation. We present an updated ad-hoc meta-analysis of randomized clinical trials in which the association between prolonged cardiac monitoring and reduced risk of ischemic stroke was nonsignificant (incidence rate ratio, 0.90 [95% CI, 0.71-1.15]). These findings highlight that larger and sufficiently powered randomized controlled trials of prolonged cardiac monitoring assessing the risk of stroke recurrence are needed. Meanwhile, we call for further research on AFDAS and stroke recurrence, and a tailored approach when using prolonged cardiac monitoring after ischemic stroke or transient ischemic attack, focusing on patients at higher risk of AFDAS and, more importantly, at higher risk of cardiac embolism.
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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 | 0.027 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".