Definitions of Cardiogenic Shock and Indications for Temporary Mechanical Circulatory Support: Joint Consensus Report of the PeriOperative Quality Initiative and the Enhanced Recovery After Surgery Cardiac Society
Bibliographic record
Abstract
BACKGROUND: The management of patients with cardiogenic shock (CS) is complex and resource intensive, particularly given the recent surge in temporary mechanical circulatory support (tMCS) devices. This document was created to establish an approach to the assessment of CS to provide early and targeted therapies, including tMCS. METHODS: An interdisciplinary, international panel of experts, using a structured appraisal of the literature and a modified Delphi method, derived consensus regarding the assessment of CS based on pathophysiologic severity, etiology, and phenotypic clustering to guide escalation of care as well as identify those patients who might benefit from tMCS. RESULTS: Key principles included early and continuous assessment for the evolution of shock severity to guide the escalation of care as well as establishment of the cause of CS to facilitate triage and assignment of initial therapies. Phenotypic clustering is complementary and aids in prognosis. tMCS provides the greatest benefit in CS for relief of congestion refractory to medical therapy, ideally when initiated before the development of organ injury. The use of tMCS should be preceded by an interdisciplinary discussion as part of the informed consent process to establish therapeutic goals, including exit strategies. CONCLUSIONS: Based on the available literature and expert consensus, there is an opportunity to further standardize the approach to CS, including characterization based on the severity of the shock state, etiology, and further enhancement by phenotyping. Monitoring, early triage, and timely escalation of care, including the targeted initiation of tMCS, can minimize organ injury and in-hospital mortality.
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 | 0.175 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".