Cardiac monitoring for detection of atrial fibrillation after TIA: A systematic review and meta-analysis
Bibliographic record
Abstract
Background and purpose The detection rate of atrial fibrillation has not been studied specifically in transient ischemic attack (TIA) patients although extrapolation from ischemic stroke may be inadequate. We conducted a systematic review and meta-analysis to determine the rate of newly diagnosed atrial fibrillation using different methods of ECG monitoring in TIA. Methods A comprehensive literature search was performed following a pre-specified protocol the PRISMA statement. Prospective observational studies and randomized controlled trials were considered that included TIA patients who underwent cardiac monitoring for >12 h. Primary outcome was frequency of detection of atrial fibrillation ≥30 s. Analyses of subgroups and of duration and type of monitoring were performed. Results Seventeen studies enrolling 1163 patients were included. The pooled atrial fibrillation detection rate for all methods was 4% (95% CI: 2–7%). Yield of monitoring was higher in selected (higher age, more extensive testing for arrhythmias before enrolment, or presumed cardioembolic/cryptogenic cause) than in unselected cohorts (7% vs 3%). Pooled mean atrial fibrillation detection rates rose with duration of monitoring: 4% (24 h), 5% (24 h to 7 days) and 6% (>7 days), respectively. Yield of non-invasive was significantly lower than that of invasive monitoring (4% vs. 11%). Significant heterogeneity was observed among studies (I 2 =60.61%). Conclusion This first meta-analysis of atrial fibrillation detection in TIA patients finds a lower atrial fibrillation detection rate in TIA than reported for IS and TIA cohorts in previous meta-analyses. Prospective studies are needed to determine actual prevalence of atrial fibrillation and optimal diagnostic procedure for atrial fibrillation detection in TIA.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.040 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".