Multidisciplinary Code Shock Team in Cardiogenic Shock: A Canadian Centre Experience
Bibliographic record
Abstract
Background Cardiogenic shock (CS) is associated with high mortality. We report on a "Shock Team" approach of combined interdisciplinary expertise for decision making, expedited assessment, and treatment. Methods We reviewed 100 patients admitted in CS over 52 months. Patients managed under a Code Shock Team protocol (n = 64, treatment) from 2016 to 2019 were compared with standard care (n = 36, control) from 2015 to 2016. The cohort was predominantly male (78% treatment, 67% control) with a median age of 55 years (interquartile range [IQR], 43-64) for treatment vs 64 years (IQR, 48-69) for control ( P = 0.01). New heart failure was more common in the treatment group: 61% vs 36%, P = 0.02. Acute myocardial infarction comprised 13% of patients in CS. There were no significant differences between treatment and control in markers of clinical acuity, including median left ventricular ejection fraction (18% vs 20%), prevalence of moderate-severe right ventricular dysfunction (64% vs 56%), median peak serum lactate (5.3 vs 4.7 mmol/L), acute kidney injury (70% vs 75%), or acute liver injury (50% vs 31%). Inotropes, dialysis, and invasive ventilation were required in 92%, 33%, and 66% of patients, respectively. Temporary mechanical circulatory support was used in 45% of treatment and 28% of control patients ( P = 0.08). There were no significant differences in median hospital length of stay (17.5 days), 30-day survival (71%), or survival to hospital discharge (66%). Over 240 days (IQR, 14,847) of median follow-up, survival was 67% for treatment vs 42% for control (hazard ratio, 0.53; 95% confidence interval, 0.28-0.99; P = 0.03). Conclusion A multidisciplinary Code Shock Team approach for CS is feasible and may be associated with improved long-term survival.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".