Thromboembolic risk stratification in atrial fibrillation—beyond clinical risk scores
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.009 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it