Abstract 20413: Pattern of Atrial Fibrillation and the Risk of Ischemic Stroke - A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Atrial fibrillation (AF) independently increases the risk of ischemic stroke. Reports differ on whether stroke risk is similar between different AF patterns; paroxysmal or permanent. While older studies reported no difference in stroke risk between AF patterns, recent oral anticoagulant trials suggested higher stroke rates in patients with permanent AF. We performed a systematic review to explore existing literature on stroke risk by AF pattern. We systematically searched PubMed (January 1966 to April 2014) for clinical, randomized controlled trials or cohort studies that reported different AF patterns in patients before occurrence of stroke events and that reported stroke rates as a function of paroxysmal or permanent AF pattern. Two authors independently assessed eligible articles for inclusion and systematically extracted data of selected studies. A meta-analysis was performed with the included studies, and potential heterogeneity among studies was explored. Of 411 identified citations, 18 papers with 134,847 AF patients were included and were analyzed according to use of oral anticoagulants (no OAC; OAC in all patients; mixed OAC use). The risk of stroke was lower for patients with paroxysmal AF than with permanent AF in all analyses (odds ratio (OR) 0.75; 95% CI 0.61-0.93 with no OAC; OR 0.77; 95% CI 0.68-0.88 with OAC in all patients; OR 0.70; 95% CI 0.58-0.84 with mixed OAC use; figure). Substantial heterogeneity was seen among studies with mixed OAC use (I2=46%), and moderate heterogeneity among studies with OAC in all patients (I2=24%). Existing literature suggests a consistent lower stroke risk in patients with paroxysmal AF than with permanent AF. Whether AF pattern is an independent predictor of stroke or rather a reflection of patients’ stroke risk profile cannot be concluded from this review due to heterogeneous study designs and AF populations.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".