How Much Atrial Fibrillation is Enough to Warrant Oral Anticoagulation: Management of Subclinical Atrial Fibrillation?
Bibliographic record
Abstract
Stroke due to atrial fibrillation (AF) is common, the cause of significant morbidity and mortality, but is highly preventable with the appropriate use of oral anticoagulants. Recent advances in implantable and wearable electrocardiographic (ECG) technologies now allow continuous monitoring of a patient’s heart rhythm for months or years at a time. Cohort studies have shown that using such methods, it is very common to find asymptomatic, short-lasting episodes of subclinical AF. Subclinical AF is also associated with an increased risk of stroke; however, the risk is lower than with traditional, ECG-detected AF and the absolute risk appears to depend on the overall burden of AF. There is currently great uncertainty as to what duration of AF should trigger the use of oral anticoagulation in specific patient groups. Large randomized trials are underway to help clarify this issue; however, in the meantime, researchers and guideline committees have proposed some guidance to assist clinicians.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".