Bibliographic record
Abstract
Hypertension is a major modifiable risk factor contributing to the development of cardiovascular diseases, including arrhythmias. Chronic elevated blood pressure induces structural and electrophysiological remodeling of the heart, creating a substrate conducive to arrhythmic events. In hypertensive hearts, left ventricular hypertrophy, myocardial fibrosis, and altered ion channel expression disrupt normal conduction pathways, predisposing patients to both atrial and ventricular arrhythmias. Additionally, elevated sympathetic activity, impaired baroreflex sensitivity, and neurohormonal activation, particularly involving the renin-angiotensin-aldosterone system (RAAS), further exacerbate arrhythmogenic risk. These pathological changes increase heterogeneity in action potential duration, enhance automaticity, and promote triggered activity through early and delayed afterdepolarizations. Atrial fibrillation is particularly prevalent in hypertensive individuals due to atrial enlargement and interstitial fibrosis, which interfere with atrial conduction. Ventricular arrhythmias, though less common, may arise from ischemia, myocardial hypertrophy, and abnormal repolarization, leading to life-threatening complications such as sudden cardiac death. The interplay between hypertension-induced structural changes and electrophysiological dysfunction creates a vicious cycle that increases cardiac vulnerability. Understanding the underlying mechanisms of arrhythmogenesis in hypertensive hearts is crucial for risk stratification, early detection, and the development of targeted therapeutic strategies. Antihypertensive treatments, especially those modulating RAAS and sympathetic activity, have shown efficacy in reducing arrhythmic burden. Furthermore, advancements in imaging and electrophysiological mapping can aid in identifying high-risk patients and guiding interventional approaches. This review underscores the importance of integrating hypertension management with arrhythmia prevention in clinical practice, highlighting a multidisciplinary approach to improving cardiovascular outcomes.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".