Abstract 25: Paroxysmal Atrial Fibrillation is Common in Patients With Defined Etiology for Stroke: Prolonged Monitoring of Cardiac Rhythm for Detection of Atrial Fibrillation After a Cerebral Ischemic Event (PEAACE) Study
Bibliographic record
Abstract
Introduction: Atrial fibrillation (AF) related cardioembolic stroke accounts for over 20% of ischemic stroke. Recent reports using prolonged cardiac rhythm monitoring (PCRM) in cryptogenic stroke reveal paroxysmal AF (PAF) in an additional 20% of patients. We report our findings with PCRM in patients with and without cryptogenic stroke patients in whom an initial 24-h Holter was negative. Methods: Patients admitted to the stroke service with no previous history of AF and no AF on Holter monitoring were enrolled for 3 weeks of PCRM. We used a PAF predictive score to determine the risk of the arrhythmia. All studies were interpreted by the stroke team prior to final review by the cardiologist. Results: Between Sept 2012 and June 2013, 96 patients were evaluated. Over all PAF was diagnoses in 37.5 % of patients. PAF was diagnosed in 32% of patients with cryptogenic stroke and 36 % of patients where an additional etiology may account for the stroke diagnosis. The AF prediction score was not useful in the recognition of patients that were more likely to be at risk for AF. 96 of 98 recordings were correctly identified by the stroke team prior to final diagnosis by the cardiologist. Interpretation: PAF is more common in stroke patients than was previously suspected. It occurs with similar frequency in patients with and without cryptogenic stroke. Our data strongly supports the need for prolonged cardiac rhythm monitoring in all stroke patients to diagnose this important preventable cause of ischemic stroke.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".