Frequency Of Atrial Arrhythmias In Stroke: Prolonged Monitoring Of Cardiac Rhythm For Detection Of Atrial Fibrillation After A Cerebral Ischemic Event (PEAACE) Study (P1.130)
Bibliographic record
Abstract
Background: Recent studies suggest that prolonged cardiac monitoring may identify paroxysmal atrial fibrillation (PAF) in up to 18% of patients with cryptogenic stroke.PAF may occur in patient where an additional etiology may account for the cause of stroke .We prospectively monitored patients presenting with a TIA or stroke in whom preliminary investigations (including a Holter) did not show atrial fibrillation. Methods: Prospective non-randomized study of patients with TIA and acute stroke between September 2012 and September 2013 where Spider Flash-t™ Monitors (Sorin) were attached to the patients for prolonged monitoring. Clinical events recordings were initially reviewed by the study team and then by the study cardiologist. The duration and frequency of PAF was recorded and the results were conveyed to the referring stroke neurologist for possible change in treatment. Results: In 102 patients (duration of monitoring 14 (±4) days), there were 39 patients (38.2%) with PAF (AF >30 seconds 12 patients, AF <30 sec 27 patients).Atrial flutter was seen in 4(3.92%) patients and Paroxysmal atrial tachycardia was detected in additional 12 patients. In 4 patients with PAF, concomitant symptomatic large vessel carotid disease was seen. The diagnosis of PAF (AF and Atrial flutter of any duration) leads to initiation of anticoagulant treatment in 31 patients. Conclusions: Prolonged cardiac monitoring for detection of atrial arrhythmias increases the yield for PAF. Our study shows that PAF is very common in stroke or TIA patients. There was no significant difference in presentation and risk factor profile of patients with less than or more than 30 seconds PAF. The arbitrary 30 seconds duration rule may have to be reconsidered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".