Endothelial dysfunction and vascular maladaptation in atrial fibrillation
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most common arrhythmia and is associated with worsened morbidity and mortality. The prevalence of AF is estimated to increase with an ageing population resulting in an ever-increasing burden on the healthcare system. Despite improvements in AF treatment, several questions remain unanswered in relation to the development and progression of AF. In this review, we discuss the evidence supporting the presence of vascular dysfunction in the development of AF, but also as a final common pathway explaining why AF constitutes a markedly increased risk of cardiovascular morbidity and mortality. Specifically, we summarise the work performed in humans related to the impact of AF on vascular structure and function, and whether measures of vascular function predict AF progression and the development of cardiovascular events. Subsequently, we discuss the potential mechanisms linking AF to the development of vascular dysfunction. Finally, we propose future perspectives of vascular health and AF, advocating a strong focus on regular exercise training as a safe and effective strategy to improve vascular function and, hence, reduce the risk for development and progression of AF and its associated risk for cardiovascular events.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".