Need for a Cardiogenic Shock Team Collaborative—Promoting a Team‐Based Model of Care to Improve Outcomes and Identify Best Practices
Bibliographic record
Abstract
Cardiogenic shock continues to carry a high mortality rate despite contemporary care, with no breakthrough therapies shown to improve survival over the past few decades. It is a time-sensitive condition that commonly results in cardiovascular complications and multisystem organ failure, necessitating multidisciplinary expertise. Managing patients with cardiogenic shock remains challenging even in well-resourced settings, and an important subgroup of patients may require cardiac replacement therapy. As a result, the idea of leveraging the collective cognitive and procedural proficiencies of multiple providers in a collaborative, team-based approach to care (the "shock team") has been advocated by professional societies and implemented at select high-volume clinical centers. A slowly maturing evidence base has suggested that cardiogenic shock teams may improve patient outcomes. Although several registries exist that are beginning to inform care, particularly around therapeutic strategies of pharmacologic and mechanical circulatory support, none of these are currently focused on the shock team approach, multispecialty partnership, education, or process improvement. We propose the creation of a Cardiogenic Shock Team Collaborative-akin to the successful Pulmonary Embolism Response Team Consortium-with a goal to promote sharing of care protocols, education of stakeholders, and discovery of how process and performance may influence patient outcomes, quality, resource consumption, and costs of care.
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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 | 0.047 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".