High Frequency of Short Paroxysms of Newly Detected Atrial Fibrillation after Stroke and TIA. Systematic Review and Meta-Analysis (P1.054)
Bibliographic record
Abstract
OBJECTIVE: We performed a systematic review and meta-analysis to investigate the frequency of newly detected atrial fibrillation lasting less than 30 seconds in stroke and transient ischemic attack patients. BACKGROUND: Current guidelines suggest that only post-stroke atrial fibrillation episodes lasting 30 seconds or longer should be considered for anticoagulation. However, there is lack of evidence supporting this recommendation. DESIGN/METHODS: We searched PubMed, Embase, and Scopus from 1980 to June 30, 2014 for studies reporting the detection of post-stroke atrial fibrillation of shorter than 30 seconds and of 30 seconds or longer. From 28,290 titles, we identified 9 studies that were included in the random-effects meta-analysis. The primary endpoint was the proportion of screened patients diagnosed with post stroke atrial fibrillation lasting less than 30 seconds. The secondary endpoint was the proportion of patients diagnosed with post stroke atrial fibrillation shorter than 30 seconds among the overall number of patients in whom an atrial fibrillation was newly detected after stroke or transient ischemic attack. RESULTS: The random effects summary for post-stroke atrial fibrillation shorter than 30 seconds was 8.9[percnt] (95[percnt] CI 4.8-14.3, Q=8.0, p(Q)=0.43, I2=<0.001[percnt]). Patients with atrial fibrillation episodes lasting less than 30 seconds represented 58.8[percnt] (95[percnt] CI 40.4-76.0, Q=9.5, p(Q)=0.30, I2=15.3[percnt]) of all the participants among whom a post-stroke atrial fibrillation was diagnosed. CONCLUSIONS: In the context of its currently unknown clinical and prognostic significance, the high frequency of PSAFs shorter than 30 seconds found in our study should be considered a strong enough reason for investigating its stroke risk in future studies. Study Supported by: Dr. Saposnik is supported by the Distinguished Clinician Scientist Award from the Heart and Stroke Foundation of Canada (HSFC).
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.058 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".