Cardiovascular disease prevention and management in COVID-19: a clinical consensus statement of the European Association of Preventive Cardiology, the European Association of Cardiovascular Imaging, the Association of Cardiovascular Nursing & Allied Professions, the European Association of Percutaneous Cardiovascular Interventions, and the Heart Failure Association of the ESC
Bibliographic record
Abstract
The coronavirus-associated disease 2019 (COVID-19) pandemic has posed significant challenges due to the complex interplay between SARS-CoV-2 infection and cardiovascular disease. COVID-19 can trigger and exacerbate cardiovascular complications, observed both during the acute phase of infection and in the post-acute phase, with some individuals developing long-term sequelae collectively termed Long COVID. Additionally, reinfection and adverse reactions to COVID-19 vaccines may contribute to cardiovascular events. This clinical consensus statement, developed by associations of the European Society of Cardiology, aims to provide a comprehensive overview of cardiovascular prevention strategies across all stages of COVID-19. These include addressing cardiovascular risk associated with acute infection, prior infection, Long COVID, reinfection, and post-vaccination events. Key recommendations focus on preventing and managing cardiovascular manifestations in patients with acute or prior COVID-19, implementing targeted cardiovascular rehabilitation, and introducing interventions to mitigate the severity of Long COVID. The document also emphasizes lifestyle modifications and personalized therapeutic approaches to enhance patient outcomes. Given the evolving nature of COVID-19 and its long-term cardiovascular implications, ongoing research is crucial to address existing knowledge gaps, optimize preventive strategies, and improve patient care. Future studies should prioritize the individualization of preventive measures for diverse populations, refine rehabilitation strategies, and advance long-term cardiovascular care, ensuring that evidence-based practices continue to evolve alongside emerging data.
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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 | 0.124 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".