Thromboembolic risk stratification in atrial fibrillation—beyond clinical risk scores
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most common arrhythmia in the adult general population. As populations age, the global burden of AF is expected to rise. AF is associated with stroke and thromboembolic complications, which contribute to significant morbidity and mortality. As a result, it remains paramount to identify patients at elevated risk of thromboembolism and to determine who will benefit from thromboembolic prophylaxis. Conventional practice advocates the use of clinical risk scoring criteria to identify patients at risk of thromboembolic complications. These risk scores have modest discriminatory ability in many sub-populations of patients with AF, highlighting the need for improved risk stratification tools. New insights have been gained on the utility of biomarkers and imaging modalities, and there is emerging data on the importance of the identification and treatment of subclinical AF. Finally, the advent of wearable devices to detect cardiac arrhythmias pose a new and evolving challenge in the practice of cardiology. This review aims to address strategies to enhance thromboembolic risk stratification and identify challenges with current and future practice.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".