Very Short Paroxysms Account for More Than Half of the Cases of Atrial Fibrillation Detected after Stroke and TIA: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Guidelines suggest that only poststroke atrial fibrillation episodes lasting 30 s or longer should be considered for anticoagulation. However, little evidence supports this recommendation. AIMS: We performed a systematic review and meta-analysis to investigate the frequency of poststroke atrial fibrillation lasting less than 30 s in stroke and transient ischemic attack patients. METHODS: We searched PubMed, Embase, and Scopus from 1980 to June 30, 2014 for studies reporting the detection of poststroke atrial fibrillation of less than 30 s and of 30 s or longer. The primary endpoint was the proportion of screened patients diagnosed with poststroke atrial fibrillation lasting less than 30 s. The secondary endpoint was the proportion of patients diagnosed with poststroke atrial fibrillation shorter than 30 s among the overall number of patients in whom a poststroke atrial fibrillation was detected after stroke or transient ischemic attack. RESULTS: From 28 290 titles, we included nine studies in the random-effects meta-analysis. Among stroke and transient ischemic attack patients without a history of atrial fibrillation, 9·0% (95% confidence interval: 4·9-14·3) experienced episodes of poststroke atrial fibrillation shorter than 30 s. An additional 6·5% (95% confidence interval: 3·2-10·9) experienced episodes of poststroke atrial fibrillation longer than 30 s. Among all patients with poststroke atrial fibrillation, 56·3% (95% confidence interval: 37·7-74·0) had poststroke atrial fibrillation episodes shorter than 30 s during diagnostic evaluation. CONCLUSIONS: The clinical and prognostic significance of poststroke atrial fibrillation episodes shorter than 30 s is unknown. The high frequency of poststroke atrial fibrillation episodes shorter than 30 s justify further investigation into the risk of stroke recurrence and the risk-benefit profile of anticoagulation for this patient population.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.054 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".