Incorporating edoxaban into the choice of anticoagulants for atrial fibrillation
Bibliographic record
Abstract
The non-vitamin K antagonist oral anticoagulants (NOACs) are replacing warfarin for stroke prevention in many patients with nonvalvular atrial fibrillation. Edoxaban, an oral factor Xa inhibitor, is the newest entrant in this class. Results of the Effective Anticoagulation with Factor Xa Next Generation in Atrial Fibrillation (ENGAGE AF) study demonstrate that edoxaban is noninferior to warfarin for prevention of stroke and systemic embolic events, and is associated with significantly less major bleeding, including intracranial bleeding, and reduced cardiovascular mortality. With a net clinical benefit over warfarin, edoxaban is well positioned as a choice among the NOACs, which include dabigatran, rivaroxaban, and apixaban. But how will clinicians choose amongst them? The purpose of this paper is to (a) place the ENGAGE AF trial results into context with results of the studies with the other NOACs, and (b) aid clinicians in selection of the right anticoagulant for the right atrial fibrillation patient.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".