Electrocardiographic Monitoring for Detecting Atrial Fibrillation After Ischemic Stroke or Transient Ischemic Attack
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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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.016 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".