Atrial disease and heart failure: the common soil hypothesis proposed by the Heart Failure Association of the European Society of Cardiology
Bibliographic record
Abstract
A proposed conceptual framework for understanding atrial disease. Subclinical atrial disease develops under the effect of stressors such as aging, cardio-metabolic risk factors and diseases, and genetic predisposition that activate pathogenic mechanisms, such as inflammation, endothelial and microvascular dysfunction, fibrosis, hypercoagulability and atrial stretch that in turn affect the atrial myocardium. Subclinical atrial disease is characterized by structural, electrical and functional changes, also termed atrial remodelling, that progress to overt clinical disease, manifesting as atrial fibrillation, heart failure and further to thromboembolism. The potential detection of subclinical atrial disease with imaging, biomarkers and other modalities offers a window opportunity for interventions that would prevent deterioration to clinical disease and could potentially allow reversal of subclinical disease. CMR, cardiac magnetic resonance; CT, computed tomography; ECG, electrocardiogram; RF, risk factors. A proposed conceptual framework for understanding atrial disease. Subclinical atrial disease develops under the effect of stressors such as aging, cardio-metabolic risk factors and diseases, and genetic predisposition that activate pathogenic mechanisms, such as inflammation, endothelial and microvascular dysfunction, fibrosis, hypercoagulability and atrial stretch that in turn affect the atrial myocardium. Subclinical atrial disease is characterized by structural, electrical and functional changes, also termed atrial remodelling, that progress to overt clinical disease, manifesting as atrial fibrillation, heart failure and further to thromboembolism. The potential detection of subclinical atrial disease with imaging, biomarkers and other modalities offers a window opportunity for interventions that would prevent deterioration to clinical disease and could potentially allow reversal of subclinical disease. CMR, cardiac magnetic resonance; CT, computed tomography; ECG, electrocardiogram; RF, risk factors.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".