The Role of a Cardiogenic Shock Team in Decision Making Surrounding Mechanical Circulatory Support
Bibliographic record
Abstract
Cardiogenic shock (CS) confers high mortality rates and remains a challenge for cardiovascular specialists. The difficulty in treating CS lies in its complexity, phenotypic heterogeneity, and the need for expedient treatment. Emerging evidence suggests that cardiogenic shock teams (CS teams), consisting of multiple specialists working in tandem with set protocols and care pathways to offer standardized team-based care, may reduce mortality and morbidity in patients with CS. A key reason for improved outcomes may be the team's decisions surrounding the use of temporary mechanical support devices (tMCS). CS teams expedite the identification of patients who require tMCS and determine the most appropriate device based on patient factors, including shock phenotype. The CS team ensures that tMCS best practices are followed and assists in determining the timing of device escalation or de-escalation. This article will discuss the rationale and role of CS teams. The evidence behind CS teams and their impact on tMCS decision making will be reviewed. Recent trial evidence for the use of tMCS in CS secondary to acute myocardial infarction (AMI) will be examined. Considerations for creating and optimizing an AMI-CS team will be highlighted. Finally, we will examine the current use of CS teams, potential challenges, and future directions for establishing CS teams in Canada.
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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 | 0.004 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".