Breaking barriers, bridging gaps, and redefining acute cardiovascular care: May issue highlights
Bibliographic record
Abstract
Welcome to the transformative May issue of the European Heart Journal—Acute Cardiovascular Care. This month’s edition is all about challenging old thinking, addressing critical gaps, and giving clinicians, researchers, and healthcare leaders the fresh evidence they need to move patient care forward. In today’s fast-paced healthcare world, especially in cardiovascular intensive care, patients are becoming more complex, and technologies are evolving rapidly. This issue not only provides answers but also raises bold questions that demand attention. From the frontline of intensive care units to the growing world of telemedicine and digital tools, we are delivering new, data-driven insights that are poised to shape the future of acute cardiovascular care. We begin this issue with a provocative study by Ali et al.,1 who shine a light on a surprising problem in clinical research. Their investigation reveals that many intensive care randomized controlled trials (RCTs) exclude the very patients who are at the heart of cardiovascular care. After analysing 412 RCTs published between 2007 and 2019, they found that 32% explicitly excluded patients with cardiovascular disease. Even when trials included some patients with heart disease, the numbers were shockingly low—only 13.2% reported having ischaemic heart disease, and 10.2% had heart failure. This profile is in stark contrast to real-world cardiac intensive care units, where coronary artery disease and heart failure rates are much higher at 41.6 and 36.2%, respectively. Why does this matter? It means that the results of these trials do not reflect the reality faced by doctors treating critically ill cardiac patients. Ali and his team highlight how trial exclusion criteria—often without clear justification—make it harder to apply the findings to everyday clinical practice, limiting their usefulness. Their work calls on the research community to rethink how critical care trials are designed and ensure heart patients are properly represented. We are deeply grateful to Dr Joseph E. Parrillo for generously contributing the editorial to this manuscript. His esteemed expertise and thoughtful perspective have added meaningful depth, and his continued influence remains a source of inspiration for the critical care community2
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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 | 0.022 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.021 | 0.031 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".