‘Shocking’ disparities and promising prognostics: advances in resuscitation science
Bibliographic record
Abstract
As acute cardiovascular care evolves, so too does our understanding of post-cardiac arrest recovery.1,2 Cardiogenic shock remains a formidable foe, affecting up to 10% of patients with acute myocardial infarction with mortality rates stubbornly above 40%, despite advances in mechanical support and pharmacotherapy.3–6 Its multifactorial causes -from ischaemic injury and arrhythmias to systemic inflammation- create a complex clinical puzzle that demands nuanced approaches.7 In this October issue of EHJ–Acute Cardiovascular Care, we convene 4 pioneering original research papers that probe the nuanced determinants shaping survivors’ outcomes—from persistent sex-based quality-of-life gaps to novel ECG prognostic markers, and from extended extracorporeal CPR windows to the unexpected promise of early repolarization patterns (ERP). Guided by an accompanying editorial by Johannes Grand and Jeanine Poss,8 we invite readers to explore how these findings challenge entrenched assumptions, sharpen clinical decision-making, and point the way toward more personalized, data-driven resuscitation strategies. Complementing this research, Mauro Riccardi and colleagues9 deliver a comprehensive educational review on acute kidney injury in cardiac patients—equipping clinicians with the latest tools for renal support at the bedside.
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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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".