Cardiogenic Shock Teams and Centres: A Contemporary Review of Multidisciplinary Care for Cardiogenic Shock
Bibliographic record
Abstract
Cardiogenic shock (CS) portends high morbidity and mortality in the contemporary era. Despite advances in temporary mechanical circulatory supports (MCS), their routine use in CS to improve outcomes has not been established. Delays in diagnosis and timely delivery of care, disparities in accessing adjunct therapies such revascularization or MCS, and lack of a systematic approach to care of CS contribute to the poor outcomes observed in CS patients. There is growing interest for developing a standardized multidisciplinary team-based approach in the management of CS. Recent prospective studies have shown feasibility of CS teams in improving survival across a spectrum of CS presentations. Herein, we will review the rationale for CS teams focusing on evidence supporting its use in streamlining care, optimizing revascularization strategies, and patient identification and MCS selection. The proposed structure and flow of CS teams will be outlined. An in-depth analysis of four recent studies demonstrating improved outcomes with CS teams is presented. Finally, we will explore potential implementation hurdles and future directions in refining and widespread implementation of dedicated cross-specialty CS teams.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".