Comprehensive risk reduction in patients with atrial fibrillation: emerging diagnostic and therapeutic options--a report from the 3rd Atrial Fibrillation Competence NETwork/European Heart Rhythm Association consensus conference
Bibliographic record
Abstract
While management of atrial fibrillation (AF) patients is improved by guideline-conform application of anticoagulant therapy, rate control, rhythm control, and therapy of accompanying heart disease, the morbidity and mortality associated with AF remain unacceptably high. This paper describes the proceedings of the 3rd Atrial Fibrillation NETwork (AFNET)/European Heart Rhythm Association (EHRA) consensus conference that convened over 60 scientists and representatives from industry to jointly discuss emerging therapeutic and diagnostic improvements to achieve better management of AF patients. The paper covers four chapters: (i) risk factors and risk markers for AF; (ii) pathophysiological classification of AF; (iii) relevance of monitored AF duration for AF-related outcomes; and (iv) perspectives and needs for implementing better antithrombotic therapy. Relevant published literature for each section is covered, and suggestions for the improvement of management in each area are put forward. Combined, the propositions formulate a perspective to implement comprehensive management 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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".