Implementation of a Multidisciplinary Cardiogenic Shock Team in a Nonacademic Canadian Heart Centre: An Implementation Study
Bibliographic record
Abstract
Background In this study we evaluated our ability to implement team-based cardiogenic shock (CS-Team), focussing on: 1) early screening; 2) CS-Team activation; and 3) use of invasive monitoring to guide therapy. Methods All patients admitted to the coronary care unit (CCU) over 12 months were screened for CS. A diagnosis of CS was made when both hypotension and hypoperfusion were present. The CS-Team was composed of the CCU attending, an interventional cardiologist, and a cardiac surgeon. Multivariate analysis was carried out with mortality as the outcome of interest. Results Screening was documented in 74% (1160 of 1562) of patients admitted to a critical care unit; of these, 1080 were not in CS. We identified 80 patients in CS (Society for Cardiovascular Angiography & Interventions [SCAI] stages C-E), which represented 6.9% of all screened patients. Patients in CS had significantly higher in-hospital mortality (35% vs 2%, P < 0.0001). CS-Team was activated in 35 of 80 patients (44%). CS-Team activation resulted in significantly greater use of invasive monitoring (pulmonary artery catheter [49% vs 7%, P < 0.0001], cardiac catheterization [94% vs 76%, P < 0.032], and mechanical circulatory support [51% vs 2%, P < 0.001]). Independent predictors of mortality were severity of CS (SCAI grades D or E) (odds ratio [OR] 18.78, 95% confidence interval [CI] 4.89-96.65) and age, in years (OR 1.07, 95% CI 1.01-1.14), whereas CS-Team was not predictive of mortality (OR 0.66, 95% CI 0.16-2.41). Conclusions We found that: 1) early screening by frontline staff was feasible but had limitations (26% screening failure); 2) CS-Team activation appeared discretionary (limited activation to 45% of patients); and 3) CS-Team activation resulted in a significant increase in the use of invasive monitoring that helped guide therapy.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".