Personalized management of atrial fibrillation: Proceedings from the fourth Atrial Fibrillation competence NETwork/European Heart Rhythm Association consensus conference
Bibliographic record
Abstract
The management of atrial fibrillation (AF) has seen marked changes in past years, with the introduction of new oral anticoagulants, new antiarrhythmic drugs, and the emergence of catheter ablation as a common intervention for rhythm control. Furthermore, new technologies enhance our ability to detect AF. Most clinical management decisions in AF patients can be based on validated parameters that encompass type of presentation, clinical factors, electrocardiogram analysis, and cardiac imaging. Despite these advances, patients with AF are still at increased risk for death, stroke, heart failure, and hospitalizations. During the fourth Atrial Fibrillation competence NETwork/European Heart Rhythm Association (AFNET/EHRA) consensus conference, we identified the following opportunities to personalize management of AF in a better manner with a view to improve outcomes by integrating atrial morphology and damage, brain imaging, information on genetic predisposition, systemic or local inflammation, and markers for cardiac strain. Each of these promising avenues requires validation in the context of existing risk factors in patients. More importantly, a new taxonomy of AF may be needed based on the pathophysiological type of AF to allow personalized management of AF to come to full fruition. Continued translational research efforts are needed to personalize management of this prevalent disease in a better manner. All the efforts are expected to improve the management of patients with AF based on personalized therapy.
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 | 0.030 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".