Cardiogenic shock trajectories: is the Society for Cardiovascular Angiography and Interventions definition the right one?
Bibliographic record
Abstract
PURPOSE OF REVIEW: We review the current Society for Cardiovascular Angiography and Interventions (SCAI) cardiogenic shock classification system and consider alternatives or iterations that may enhance our current descriptions of cardiogenic shock trajectory. RECENT FINDINGS: Several studies have identified the potential prognostic value of serial SCAI stage re-assessment, usually within the first 24 h of shock onset, to predict deterioration and clinical outcomes across shock causes. In parallel, numerous registry-based analyses support the utility of a more precise assessment of the macrocirculation and microcirculation, leveraging invasive haemodynamics, imaging and additional laboratory and clinical markers. The emergence of machine learning and artificial intelligence capabilities offers the opportunity to integrate multimodal data into high fidelity, real-time metrics to more precisely define trajectory and inform our therapeutic decision making. SUMMARY: Whilst the SCAI staging system remains a pivotal tool in cardiogenic shock assessment, communication and reassessment, it is vital that the sophistication with which we measure and assess shock trajectory evolves in parallel our understanding of the complexity and variability of clinical course and clinical outcomes.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".