Efficacy of Shock Team Implementation for Cardiogenic Shock: A Systematic Review
Bibliographic record
Abstract
Abstract Introduction Cardiogenic shock (CS) is a critical cardiac condition characterized by low cardiac output leading to end-organ hypoperfusion and associated with high in-hospital mortality rates. It can manifest following acute myocardial infarction or acute exacerbation of chronic heart failure. Despite advancements, mortality rates remain elevated, prompting interest in multidisciplinary approaches to improve outcomes. This manuscript presents a review focused on the concept of a CS team and its potential impact on patient management and outcomes. Methods A comprehensive search was performed on March 19th, 2023, covering PubMed, Web of Science, Scopus, Embase, and Cochrane Library. We included primary studies (prospective and retrospective) only and evaluated their quality using the Newcastle-Ottawa Quality Scale. This review was registered in PROSPERO ( CRD42023440354 ). Results Six relevant studies with 2066 CS patients were included, of which 1071 were managed by shock teams and 995 received standard care. Findings from the reviewed studies indicated the favorable outcomes associated with implementing CS teams. Patients managed by these teams exhibited higher 30-day and in-hospital survival rates compared to those without team intervention. The implementation of CS teams was linked to reduced in-hospital and ICU mortality rates. Additionally, shock team involvement was associated with shorter door-to-balloon times. Conclusion This review highlighted the positive influence of CS teams on patient care, enabling early detection, timely interventions, and shorter ICU stays. Despite implementation challenges, CS teams hold promise for improving management outcomes, necessitating increased attention and ongoing research in multidisciplinary strategies to advance CS care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".