Electrocardiographic Monitoring for Detecting Atrial Fibrillation After Ischemic Stroke or Transient Ischemic Attack
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is a major cause of stroke. Although standard investigations after an event include electrocardiographic monitoring, the optimal duration to detect AF is unclear. We performed a systematic review and meta-analysis to determine whether the duration of electrocardiographic monitoring after an ischemic event is related to the detection of AF. METHODS AND RESULTS: Prospective studies that reported the proportion of new AF diagnosed using electrocardiographic monitoring for > 12 hours in patients with recent stroke or transient ischemic attack were analyzed. Studies were excluded if the stroke was hemorrhagic or AF was previously diagnosed. A total of 31 articles met inclusion criteria. Longer duration of monitoring was associated with an increased detection of AF when examining monitoring time as a continuous variable (P < 0.001 for metaregression analysis). When dichotomizing studies based on monitoring duration, studies with monitoring lasting ≤ 72 hours detected AF in 5.1%, whereas monitoring lasting ≥ 7 days detected AF in 15%. The proportion of new diagnosis increased to 29.15% with extended monitoring for 3 months. Significant heterogeneity within studies was detected for both groups (≤ 72 hours, I(2) = 91.3%; ≥ 7 days, I(2) =7 5.8). When assessing the odds of AF detection in the 3 randomized controlled trial, there was a 7.26 increased odds of AF with long-term monitoring (95% confidence intervals [3.99-12.83]; P value < 0.001). CONCLUSIONS: Longer duration of electrocardiographic monitoring after cryptogenic stroke is associated with a greater detection of AF. Future investigation is needed to determine the optimal duration of long-term monitoring.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it