Abstract 11908: Organizational Structure, Resources, and Educational Initiatives in Cardiac Intensive Care Units: A National Cross-sectional Survey on Behalf of the American Heart Association Acute Cardiac Care Committee
Bibliographic record
Abstract
Introduction: The acuity and medical complexity of patients admitted to cardiac intensive care units (CICUs) is increasing; yet, little is known about contemporary CICU organizational structures, staffing, resources, and educational activities in the United States Methods: A cross-sectional, 16-question web-based survey was distributed electronically (October 2015) to AHA Mission: Lifeline coordinators at 542 registered hospitals with a request for completion by the CICU director or unit manager. Non-responders were contacted by email and phone. Results: A total of 138 hospitals (25.5% response rate) had completed the survey by May 1, 2016 (17.4% academic, 20.3% tertiary non-academic, and 62.3% community hospitals). Most CICUs were open units (76.8%) with multiple simultaneous responsible attending physicians. The Figure shows variation in CICU patient populations, organizational structure, medical leadership and role of intensivists. Overall, 61% of centers participated in clinical training of housestaff and/or advanced practice providers, while only 8.5% of centers had a dedicated Cardiac Critical Care subspecialty training program. 12.3% of all CICUs had the organizational structure and on-site resources to be classified as a Level 1 CICU by 2012 American Heart Association Scientific Statement on CICU medical staffing and training models. Conclusions: Among US hospitals, there was substantial variability in CICU organizational structure, practice, and educational activities. Approximately 1 in 10 CCUs had on-site resources necessary matching a Level 1 CICU classification. These data inform the current CICU landscape and may help to identify potential areas for organizational improvement and resource allocation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".