Modern Cardiac ICU Care Delivery and the Role of the Cardiac ICU Cardiologist
Bibliographic record
Abstract
BACKGROUND: The cardiac intensive care unit (CICU) has evolved into a complex care environment for critically ill patients with cardiac and noncardiac diseases. OBJECTIVES: Our goal was to describe contemporary CICU care delivery and the role of cardiologists therein. METHODS: The American College of Cardiology administered a 42-item survey to U.S. and Canadian CICU-focused cardiologists designed to capture models of care delivery and workforce demographics. RESULTS: The survey was distributed by email to 1,085 U.S. and Canadian CICU cardiologists. The response rate was 20%, yielding a final sample of 166 after excluding trainees and those not board-certified or board-eligible in cardiology. The majority were from medium (34%) or large (64%) academic (81%) medical centers. Fifty-three percent reported working in high-intensity care models and 61% reported that their CICU was dedicated exclusively to medical cardiology patients. Critical care medicine-boarded physicians contributed to care through consultative (53%), comanagement (29%), and/or primary roles (44%). Subspecialization beyond cardiology was common (82%), with critical care medicine being most frequent (46%), followed by echocardiography (37%), advanced heart failure (21%), and interventional cardiology (16%). Limitations include the low survey response rate, which raises the risk of selection bias. CONCLUSIONS: This study provides insight into the current landscape of cardiac critical care delivery in North America, highlighting wide variation in staffing models, subspecialty training, and clinical practice. Our findings highlight growing trends toward high-intensity staffing models that incorporate critical care medicine-boarded physicians in consultative, comanagement, and or primary roles.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".