Subclinical Atrial Fibrillation and Risk of Stroke: Past, Present and Future
Bibliographic record
Abstract
Subclinical atrial fibrillation (SCAF) describes asymptomatic episodes of atrial fibrillation (AF) that are detected by cardiac implantable electronic devices (CIED). The increased utilization of CIEDs renders our understanding of SCAF important to clinical practice. Furthermore, 20% of AF present initially as a stroke event and prolonged cardiac monitoring of stroke patients is likely to uncover a significant prevalence of SCAF. New evidence has shown that implanting cardiac monitors into patients with no history of atrial fibrillation but with risk factors for stroke will yield an incidence of SCAF approaching 30-40% at around three years. Atrial high rate episodes lasting longer than five minutes are likely to represent SCAF. SCAF has been associated with an increased risk of stroke that is particularly significant when episodes of SCAF are greater than 23 h in duration. Longer episodes of SCAF are incrementally more likely to progress to episodes of SCAF >23 h as time progresses. While only around 30-40% of SCAF events are temporally related to stroke events, the presence of SCAF likely represents an important risk marker for stroke. Ongoing trials of anticoagulation in patients with SCAF durations less than 24 h will inform clinical practice and are highly anticipated. Further studies are needed to clarify the association between SCAF and clinical outcomes as well as the factors that modify this association.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".