Abstract P630: Detection of Atrial Fibrillation by Prolonged Cardiac Monitoring in Cryptogenic and Known Stroke Etiology
Bibliographic record
Abstract
Introduction: Approximately 25% of strokes are classified as cryptogenic (CS), while greater than 50% have an identifiable or ‘known’ etiology (KS). Several studies have demonstrated that prolonged cardiac monitoring (PCM) after cryptogenic stroke substantially increases the detection of atrial fibrillation (AF), but the yield of PCM in KS stroke is unknown. As a result, the majority of guidelines recommend restricting PCM to patients with cryptogenic stroke. If the detection of AF in KS is no different to cryptogenic stroke, it would suggest that this group too would similarly benefit from PCM, with the potential to impact therapeutic decisions (e.g. initiating anticoagulation). Methods: In a cross-sectional study, we compared AF detection by PCM (minimum of 48 hrs) between CS and KS patients without a previous diagnosis of AF. We developed a multivariate logistic regression model by including known and significant clinical, echocardiographic, and radiological factors known to be associated with the detection of AF. We reported results as odds ratios (OR) and 95% confidence intervals (95% CI). Results: We included 561 ischemic stroke patients, 376 with CS and 185 with KS. The median duration of PCM was 167h for CS and 48h for KS. AF was detected in 30 of 376 (8%) CS patients, and 20 of 185 (7.9%) KS patients. Age, history of thyroid disease, clinical presentation of dysarthria, wake-up stroke, and left atrial volume index on echocardiography were significantly associated with a new diagnosis of AF after stroke in the univariable analysis and were thus included in the logistic regression analysis. Additionally, duration of PCM was included in the multivariate model. After adjustment for potential confounders, AF detection by PCM was not significantly higher for CS than KS (OR 0.95, 95% CI 0.25-3.32, P=0.94). Conclusion: To the best of our knowledge this is the first study directly comparing the incidence of AF between CS and KS as the pre-specified primary outcome. Our findings suggest that CS and KS patients have similar rates of AF detection by PCM. Future prospective research is required to confirm these findings and to determine the cost-effectiveness of PCM in non-cryptogenic stroke patients.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".