Risk Prediction in Cardiogenic Shock: Current State of Knowledge, Challenges and Opportunities
Bibliographic record
Abstract
Cardiogenic shock (CS) is a condition associated with high mortality rates in which prognostication is uncertain for a variety of reasons, including its myriad causes, its rapidly evolving clinical course and the plethora of established and emerging therapies for the condition. A number of validated risk scores are available for CS prognostication; however, many of these are tedious to use, are designed for application in a variety of populations and fail to incorporate contemporary hemodynamic parameters and contemporary mechanical circulatory support interventions that can affect outcomes. It is important to separate patients with CS who may recover with conservative pharmacological therapies from those in who may require advanced therapies to survive; it is equally important to identify quickly those who will succumb despite any therapy. An ideal risk-prediction model would balance incorporation of key hemodynamic parameters while still allowing dynamic use in multiple scenarios, from aiding with early decision making to device weaning. Herein, we discuss currently available CS risk scores, perform a detailed analysis of the variables in each of these scores that are most predictive of CS outcomes and explore a framework for the development of novel risk scores that consider emerging therapies and paradigms for this challenging clinical entity.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".