The detection and treatment of subclinical atrial fibrillation: evaluating the IMPACT of a comprehensive strategy based on remote arrhythmia monitoring
Bibliographic record
Abstract
This editorial refers to ‘Randomized trial of atrial arrhythmia monitoring to guide anticoagulation in patients with implanted defibrillator and cardiac resynchronization devices’†, by D.T. Martin et al., on page 1660. Atrial fibrillation (AF) is the most common clinical arrhythmia, which is responsible for at least 15% of all strokes, and is the leading cause of stroke among patients >75 years old.1 Moreover, strokes due to AF are largely avoidable, as the use of oral anticoagulants (OACs),2,3 along with the diagnosis and treatment of hypertension,4 can prevent >65% of all strokes. The results of large cohort studies in pacemaker patients demonstrate that AF is frequently ‘subclinical’ (either asymptomatic or which evades clinical detection),5,6 suggesting that the true burden of AF in patients over the age of 65 years is much greater than previously appreciated. These studies also show that subclinical AF (Figure 1) is associated with up to a 2.5-fold increase in the risk of stroke,5,7 highlighting the importance of recognizing this condition in patients with pacemakers and implantable cardioverter defibrillators (ICDs).
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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 | 0.011 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".