Differences between Atrial Fibrillation Detected before and after Stroke and TIA: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Preliminary evidence suggests that patients with atrial fibrillation (AF) detected after stroke (AFDAS) may have a lower prevalence of cardiovascular comorbidities and lower risk of stroke recurrence than AF known before stroke (KAF). OBJECTIVE: We performed a systematic search and meta-analysis to compare the characteristics of AFDAS and KAF. METHODS: We searched PubMed, Scopus, and EMBASE for articles reporting differences between AFDAS and KAF until June 30, 2021. We performed random- or fixed-effects meta-analyses to evaluate differences between AFDAS and KAF in demographic factors, vascular risk factors, prevalent vascular comorbidities, structural heart disease, stroke severity, insular cortex involvement, stroke recurrence, and death. RESULTS: In 21 studies including 22,566 patients with ischemic stroke or transient ischemic attack, the prevalence of coronary artery disease, congestive heart failure, prior myocardial infarction, and a history of cerebrovascular events was significantly lower in AFDAS than KAF. Left atrial size was smaller, and left ventricular ejection fraction was higher in AFDAS than KAF. The risk of recurrent stroke was 26% lower in AFDAS than in KAF. There were no differences in age, sex, stroke severity, or death rates between AFDAS and KAF. There were not enough studies to report differences in insular cortex involvement between AF types. CONCLUSIONS: We found significant differences in the prevalence of vascular comorbidities, structural heart disease, and stroke recurrence rates between AFDAS and KAF, suggesting that they constitute different clinical entities within the AF spectrum. PROSPERO registration number is CRD42020202622.
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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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".