World Heart Federation Roadmap on Atrial Fibrillation – A 2020 Update
Bibliographic record
Abstract
The World Heart Federation (WHF) commenced a Roadmap initiative in 2015 to reduce the global burden of cardiovascular disease and resultant burgeoning of healthcare costs. Roadmaps provide a blueprint for implementation of priority solutions for the principal cardiovascular diseases leading to death and disability. Atrial fibrillation (AF) is one of these conditions and is an increasing problem due to ageing of the world's population and an increase in cardiovascular risk factors that predispose to AF. The goal of the AF roadmap was to provide guidance on priority interventions that are feasible in multiple countries, and to identify roadblocks and potential strategies to overcome them. Since publication of the AF Roadmap in 2017, there have been many technological advances including devices and artificial intelligence for identification and prediction of unknown AF, better methods to achieve rhythm control, and widespread uptake of smartphones and apps that could facilitate new approaches to healthcare delivery and increasing community AF awareness. In addition, the World Health Organisation added the non-vitamin K antagonist oral anticoagulants (NOACs) to the Essential Medicines List, making it possible to increase advocacy for their widespread adoption as therapy to prevent stroke. These advances motivated the WHF to commission a 2020 AF Roadmap update. Three years after the original Roadmap publication, the identified barriers and solutions were judged still relevant, and progress has been slow. This 2020 Roadmap update reviews the significant changes since 2017 and identifies priority areas for achieving the goals of reducing death and disability related to AF, particularly targeted at low-middle income countries. These include advocacy to increase appreciation of the scope of the problem; plugging gaps in guideline management and prevention through physician education, increasing patient health literacy, and novel ways to increase access to integrated healthcare including mHealth and digital transformations; and greater emphasis on achieving practical solutions to national and regional entrenched barriers. Despite the advances reviewed in this update, the task will not be easy, but the health rewards of implementing solutions that are both innovative and practical will be great.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.028 |
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".