Early and comprehensive management of atrial fibrillation: executive summary of the proceedings from the 2nd AFNET-EHRA consensus conference ‘research perspectives in AF’
Bibliographic record
Abstract
Atrial fibrillation (AF) causes important mortality and morbidity on a population-level. So far, we do not have the means to prevent AF or AF-related complications adequately. Therefore, over 70 experts on atrial fibrillation convened for the 2nd AFNET/EHRA consensus conference to suggest directions for research to improve management of AF patients (Appendix 1). The group defined three main areas in need for research in AF: 1. better understanding of the mechanisms of AF; 2. Improving rhythm control monitoring and management; and 3. comprehensive cardiovascular risk management in AF patients. The group put forward the hypothesis that successful therapy of AF and its associated complications will require comprehensive therapy. This applies e.g. to the "old" debate of "rate versus rhythm control", since rhythm control is generally added to underlying (continued) rate control therapy, but also to the emerging debate of "antiarrhythmic drugs versus catheter ablation", of which both may be needed in most patients to maintain sinus rhythm, but also to therapy of conditions that predispose to AF and contribute to cardiovascular complications such as stroke, cognitive decline, heart failure, and acute coronary syndromes. We call for research initiatives aiming at a better understanding of the different causes of AF and its complications, and at development and validation of mechanism-based therapies. The future of AF therapy may require a combination of management of underlying and concomitant conditions, early and comprehensive rhythm control therapy, adequate control of ventricular rate and cardiac function, and continuous therapy to prevent AF-associated complications (e.g. antithrombotic therapy). The reasons for these suggestions are detailed in this paper.
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 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".