Noninvasive Cardiac Monitoring for Detecting Paroxysmal Atrial Fibrillation or Flutter After Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Identifying paroxysmal atrial fibrillation/flutter is an essential part of the etiological workup of patients with ischemic stroke. However, there is controversy in the literature regarding the use of noninvasive cardiac rhythm monitoring with previous reviews reporting a low detection rate with routine monitoring. We performed a systematic review to determine the frequency of occult atrial fibrillation/flutter detected by noninvasive methods of continuous cardiac monitoring after acute ischemic stroke or transient ischemic attack. METHODS: Studies were identified from comprehensive searches of PubMed, EMBASE, Science Citation Index, and bibliographies of relevant articles. Only English language articles were included. Randomized controlled trials and prospective cohort studies of consecutive patients with acute ischemic stroke that fulfilled predefined criteria were eligible. Two authors conducted searches and abstracted data from eligible studies independently. RESULTS: Sixty studies were deemed potentially eligible. After application of eligibility criteria, 5 studies (736 participants) were included in the analysis. All studies evaluated Holter monitoring; 2 also evaluated event loop recording. In studies that evaluated Holter monitoring (588 participants), new atrial fibrillation/flutter was detected in 4.6% (95% CI: 0% to 12.7%) of consecutive patients with ischemic stroke. Duration of monitoring ranged from 24 to 72 hours. Two studies (140 participants) evaluated event loop recorders after Holter monitoring. New atrial fibrillation/flutter was detected in 5.7% and 7.7% of consecutive patients in these 2 studies. CONCLUSIONS: Screening consecutive patients with ischemic stroke with routine Holter monitoring will identify new atrial fibrillation/flutter in approximately one in 20 patients. Although based on limited data, extended duration of monitoring may improve the detection rate. Further research is required before definitive recommendations can be made.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".