Definition, Classification, and Management of Primary Noncardiac Causes of Cardiogenic Shock
Bibliographic record
Abstract
Cardiogenic shock (CS) is a complex syndrome, presenting with a critical state of cardiac output insufficient to support end-organ perfusion requirements. Contemporary CS classification recognizes broad categories of primary cardiac etiologies of CS, such as acute myocardial infarction and heart failure. Primary noncardiac etiologies of CS, however, are poorly described in literature and have not been captured by any contemporary classification, leading to challenges in diagnosing and managing these cases. In this review, we propose that primary noncardiac causes of CS be recognized as its own category that builds on the original Shock Academic Research Consortium classification with its own additional modifiers. We present a detailed framework that groups each noncardiac cause by its underlying disease mechanism (vascular, infectious, inflammatory, traumatic, toxic, cancer related, endocrine, metabolic) and review available literature on their respective management strategies. We expect that the ability to classify primary noncardiac causes of CS will help with early identification and targeted management of the primary noncardiac insult, support patients through their shock state, and may lead to improvement of in-hospital CS mortality rates in clinical practice. Moreover, this new framework can further assist clinical trial classifications to properly phenotype CS for clinical research purposes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".