Integrated care of patients with atrial fibrillation: the 2016 ESC atrial fibrillation guidelines
Bibliographic record
Abstract
Atrial fibrillation (AF) is one of the evolving epidemics in cardiovascular medicine. AF is projected to develop in 25% of currently 40-year-old adults,1 ,2 provokes many, often severe strokes, is associated with increased mortality and often leads to heart failure or sudden death even in well-anticoagulated patients.3 ,4 The last few years have seen the development of new approaches to detect and treat patients with AF, from ECG screening to surgical procedures to prevent recurrent AF. While these improvements provide tools to improve outcomes and quality of life in affected patients, they often need multidisciplinary input into patient management. It therefore seems timely that the European Society of Cardiology (ESC) has issued new AF guidelines in August 2016.5 Reflecting the need for multidisciplinary input, the 2016 ESC AF guidelines task force consisted of cardiologists with different degrees of sub-specialisation, cardiac surgeons, a stroke neurologist and a specialist nurse, nominated by the ESC and its constituent bodies including the European Heart Rhythm Association, the European Association of Cardio-Thoracic Surgeons and the European Stroke Organization. All recommendations were discussed and voted upon in a predefined process, accepting only recommendations supported by at least 75% of the Task Force members after a structured consultation. Thirty-three general reviewers reviewed the entire guideline in three iterations, while 49 additional reviewers nominated by the ESC National Societies focusing on the class I and III recommendations provided further valuable comments.5 ECG screening can help detect asymptomatic AF, allowing timely initiation of therapy, especially oral anticoagulation. This has the potential to prevent complications of AF, especially ischaemic strokes. The ESC guidelines recommend opportunistic ECG screening whenever a person aged 65 years or older is seen by a healthcare professional.6 In addition, and beyond prior guidelines, systematic ECG monitoring for at least 72 hours …
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".