Transesophageal Echocardiography Risk Factors for Stroke in Nonvalvular Atrial Fibrillation
Bibliographic record
Abstract
Atrial fibrillation is a common arrhythmia, particularly in the older age groups. It confers an increased risk of thromboembolism to these patients, and multiple clinical risk factors have been identified to be useful in predicting the risks of thromboembolic events. Recent studies have evaluated the role of transesophageal echocardiography (TEE) in the evaluation of patients with atrial fibrillation. The purpose of this review is to evaluate the significance of transesophageal echocardiographic findings in the prediction of thromboembolic events, particularly stroke, in patients with nonvalvular atrial fibrillation, with an emphasis on recently reported prospective studies. Aortic plaque and left atrial appendage abnormalities are identified as independent predictors of thromboembolic events. Although they are associated with clinical events, they also have independent incremental prognostic values. Other transesophageal echocardiographic findings, such as patent foramen ovale and atrial septal aneurysm, have not been found to be predictors of thromboembolic events in this patient group. Thus, TEE is a useful tool in stratifying patients with nonvalvular atrial fibrillation into different risk groups in terms of thromboembolic events, and it will likely play an important role in future studies to assess new treatment strategies in high-risk patients with atrial fibrillation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".