Emerging therapies for stroke prevention in atrial fibrillation
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 a major risk factor for stroke. Recent studies show that treatment strategies which combine control of ventricular rate with antithrombotic therapy are as effective as those strategies aimed at restoring sinus rhythm. Current antithrombotic therapy regimens in patients with AF involve chronic anticoagulation with dose-adjusted vitamin K antagonists (VKAs), unless patients have a contraindication to these agents or are at low risk for stroke. AF patients at low risk for stroke may benefit from aspirin. Although VKAs are effective, their use is problematic, highlighting the need for new antithrombotic strategies. This paper will (i) provide an overview of the clinical trials that form the basis for current antithrombotic guidelines in patients with AF, (ii) highlight the limitations of current antithrombotic drugs used for stroke prevention, (iii) review the pharmacology of new antithrombotic drugs under evaluation in AF, (iv) describe ongoing trials with new antiplatelet therapies and idraparinux, and completed studies with ximelagatran in patients with AF, (v) discuss the role of non-pharmacological techniques to reduce the risk of stroke in AF patients, and (vi) provide clinical perspective into the potential role of new antithrombotic drugs in AF.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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