Savouring the latest in acute cardiovascular care: fresh from Madrid
Bibliographic record
Abstract
As the vibrant city of Madrid wraps up another spectacular European Society of Cardiology (ESC) Scientific Sessions, the excitement is palpable—much like savouring a perfectly prepared jamón ibérico or a sizzling tapas spread after a long day. Just as Madrid’s culinary delights blend tradition with bold innovation, the latest research in acute cardiovascular and intensive care marries tried-and-true clinical wisdom with groundbreaking discoveries. In this issue, we serve you five rich and thought-provoking original research papers that dive deep into out-of-hospital cardiac arrest (OHCA), heart failure, and novel biomarker diagnostics. From exploring quality-of-life after cardiac arrest to refining prognostic tools and uncovering electrocardiographic markers of survival, these papers offer a feast of insights that every clinician will find indispensable. But the main course is yet to come. We’re also excited to open the door to three exclusive ‘In Perspectives’ commentaries—carefully selected to spotlight the freshest, late-breaking science unveiled at ESC Madrid. Just as a well-curated paella combines the finest ingredients into a harmonious whole, these perspectives blend expert analysis and clinical relevance to help you digest the most important late-breaking trials and emerging advances in the field.
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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 | 0.018 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.013 | 0.032 |
| Insufficient payload (model declined to judge) | 0.010 | 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".