Direct oral anticoagulants for stroke prevention in patients with device-detected atrial fibrillation: assessing net clinical benefit
Bibliographic record
Abstract
Subclinical, device-detected atrial fibrillation (AF) is frequently recorded by pacemakers and other implanted cardiac rhythm devices. Patients with device-detected AF have an elevated risk of stroke, but a lower risk of stroke than similar patients with clinical AF captured with surface electrocardiogram. Two randomized clinical trials (NOAH-AFNET 6 and ARTESiA) have tested a direct oral anticoagulant (DOAC) against aspirin or placebo. A study-level meta-analysis of the two trials found that treatment with a DOAC resulted in a 32% reduction in ischaemic stroke and a 62% increase in major bleeding; the results of the two trials were consistent. The annualized rate of stroke in the control arms was ∼1%. Several factors point towards overall net benefit from DOAC treatment for patients with device-detected AF. Strokes in ARTESiA were frequently fatal or disabling and bleeds were rarely lethal. The higher absolute rates of major bleeding compared with ischaemic stroke while on treatment with a DOAC in the two trials are consistent with the ratio of bleeds to strokes seen in the pivotal DOAC vs. warfarin trials in patients with clinical AF. Prior research has concluded that patients place a higher emphasis on stroke prevention than on bleeding. Further research is needed to identify the characteristics that will help identify patients with device-detected AF who will receive the greatest benefit from DOAC treatment.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".