New horizons in acute cardiovascular care: from tiny hearts to timely diagnoses
Bibliographic record
Abstract
As we open this issue of European Heart Journal – Acute Cardiovascular Care, we celebrate a landmark moment: the journal’s inaugural focus on paediatric acute cardiovascular and intensive care. This expansion reflects not only a growing scientific interest but a clinical imperative—children with acute cardiac illnesses demand specialized, evidence-driven strategies, and until now, their needs have been underrepresented in the acute cardiovascular literature. Our attention also turns to an increasingly important population: Grown-Up Congenital Heart Disease (GUCH) or GRUNCH or Adult Congenital Heart Disease (ACHD). Thanks to advances in paediatric cardiology and surgery, more patients with congenital heart disease now survive in adulthood, bringing with them complex anatomical and physiological challenges. These patients often present acutely, requiring an integration of congenital expertise with adult intensive cardiac care—a demand that is reshaping the landscape of emergency and critical cardiovascular services across Europe. Their inclusion as the journal’s editor’s choice this month is a natural extension of our commitment to comprehensive and inclusive acute cardiovascular care. Parallel to these clinical shifts is an equally profound transformation in how we conceptualize coronary disease. As outlined in the recent Lancet publication,1 there is a growing consensus that coronary artery disease (CAD) must be reframed as atherosclerotic CAD (ACAD). Moving away from a late-stage focus on ischaemia and obstruction, this reframing promotes early detection and lifelong prevention strategies—essential tenets for reducing cardiovascular morbidity and mortality on a global scale. This shift is not merely semantic; it is foundational, advocating for a proactive, rather than reactive, model of care.
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 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 | 0.009 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.013 | 0.033 |
| Insufficient payload (model declined to judge) | 0.018 | 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".