Operating Room Codes Redefined: A Highly Reliable Model Integrating the Core Hospital Code Team
Bibliographic record
Abstract
INTRODUCTION: Typically, multidisciplinary teams manage cardiac arrests occurring outside of the operating room (OR). This approach results in reduced morbidity. However, arrests that occur in the OR are usually managed by OR personnel alone, missing the benefits of out-of-OR hospital code teams. At our institution, there were multiple pathways to activate codes, each having different respondents, depending on time and day of the week. This improvement initiative aimed to create a reliable intraoperative emergency response system with standardized respondents and predefined roles. METHODS: A multidisciplinary improvement team led this project at an academic pediatric hospital in California. After simulations performed in the OR (in situ), the team identified a valuable key driver-a consistent activation process that initiated standard respondents, 24 hours a day, 7 days a week. By utilizing core hospital code members routinely available outside of the OR during days, nights, and weekends, respondents were identified to augment OR personnel. Code roles were preassigned. After education, we conducted in situ simulations that included the perioperative and out-of-OR code team members. We administered a knowledge assessment to perioperative staff. RESULTS: The knowledge assessment for perioperative staff (n = 52) had an average score of 96%. Review of subsequent OR codes reflects an improved initiation process and management. CONCLUSIONS: The process for activating the emergency response system and roles for intraoperative code respondents were standardized to ensure a predictable code response, regardless of time or day of the week. Ongoing simulations with perioperative personnel continue to optimize the process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".