Screening for undiagnosed atrial fibrillation in the community
Bibliographic record
Abstract
PURPOSE OF REVIEW: Recent years have seen significant advances in knowledge about the prevalence of 'silent' atrial fibrillation and the morbidity associated with this condition. Data are emerging on improved strategies for screening, and new technologies for detecting atrial fibrillation are becoming available, making a review of this field timely. RECENT FINDINGS: Studies suggest that, when screening is performed, undiagnosed atrial fibrillation is present in around 1% of the screened population, rising to 1.4% for those aged at least 65 years. The prevalence of silent atrial fibrillation is even higher in patients with additional risk factors (e.g. those aged 75 years, patients with heart failure). Prolonged monitoring of patients with hypertension and an implanted cardiac device showed subclinical atrial arrhythmias in at least 10% and these patients had a 2.5-fold increased risk of stroke or systemic embolism. The feasibility of screening for silent atrial fibrillation has been demonstrated in a number of populations and many new technologies for atrial fibrillation detection exist, which could improve the efficiency and cost-effectiveness of this process. SUMMARY: Increased attention is being directed towards screening for silent atrial fibrillation and our 'toolbox' for detecting it is expanding. Whether this will translate into improved outcomes for patients remains to be proven.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".