‘November has come: breaking ground in acute cardiovascular care’
Bibliographic record
Abstract
November has come, with a new issue of EHJ–Acute Cardiovascular Care devoted to a formidable problem in medicine: acute myocardial infarction (MI). Despite remarkable advances, MI still carries a heavy burden. Ventricular tachycardia (VT), ventricular fibrillation (VF), and cardiogenic shock are among its most serious complications.1–5 This issue looks beyond the usual pathways, examining coronary embolism as a distinct cause of MI, exploring the pervasive weight of diabetes as a comorbid condition, and investigating the growing, evidence-based role of artificial intelligence (AI)—especially in electrocardiogram (ECG) interpretation—to sharpen diagnosis and timely intervention when minutes matter.6 An Acute Cardiovascular Care Association (ACVC) consensus document, broadens the view to a global scale, providing practical guidance for achieving timely reperfusion and more equitable care in low- and middle-income countries. Together, these contributions are inspiring, highlighting actionable solutions that may improve patient outcomes at the bedside and strengthen cardiovascular care across health systems. Mengmeng Li and colleagues7 present the largest contemporary experience of emergency bail-out ablation for refractory VT, occurring early after MI. Among 12 835 admissions, 261 developed VT or VF, with 51 experiencing refractory VT storm. Nineteen patients ultimately underwent ablation, with termination of VT achieved in 94.7% and survival to discharge in 18 of 19 patients. At 18 months, only one recurrence of VF was seen, while all patients without ablation died within days. These results are striking, positioning ablation as a potentially life-saving option in situations where conventional measures fail.
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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 | 0.017 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.016 | 0.033 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.018 | 0.032 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".