Interaction of Heart Failure and Stroke: A Clinical Consensus Statement of the ESC Council on Stroke, the Heart Failure Association (HFA) and the ESC Working Group on Thrombosis
Bibliographic record
Abstract
Heart failure (HF) is a major disease in our society that often presents with multiple comorbidities with mutual interaction and aggravation. The comorbidity of HF and stroke is a high risk condition that requires particular attention to ensure early detection of complications, efficient diagnostic workup, close monitoring, and consequent treatment of the patient. The bi-directional interaction between the heart and the brain is inherent in the pathophysiology of HF where HF may be causal for acute cerebral injury, and - in turn - acute cerebral injury may induce or aggravate HF via imbalanced neural and neurovegetative control of cardiovascular regulation. The present document represents the consensus view of the ESC Council on Stroke, the Heart Failure Association and the ESC Working Group on Thrombosis to summarize current insights on pathophysiological interactions of the heart and the brain in the comorbidity of HF and stroke. Principal aspects of diagnostic workup, pathophysiological mechanisms, complications, clinical management in acute conditions and in long-term care of patients with the comorbidity are presented and state-of-the-art clinical management and current evidence from clinical trials is discussed. Beside the physicians perspective, also the patients values and preferences are taken into account. Interdisciplinary cooperation of cardiologists, stroke specialists, other specialists and primary care physicians is pivotal to ensure optimal treatment in acute events and in continued long-term treatment of these patients. Key consensus statements are presented in a concise overview on mechanistic insights, diagnostic workup, prevention and treatment to inform clinical acute and continued care of patients with the comorbidity of HF and stroke.
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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 | 0.037 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".