Simulation-Based Trial of Surgical-Crisis Checklists
Bibliographic record
Abstract
BACKGROUND: Operating-room crises (e.g., cardiac arrest and massive hemorrhage) are common events in large hospitals but can be rare for individual clinicians. Successful management is difficult and complex. We sought to evaluate a tool to improve adherence to evidence-based best practices during such events. METHODS: Operating-room teams from three institutions (one academic medical center and two community hospitals) participated in a series of surgical-crisis scenarios in a simulated operating room. Each team was randomly assigned to manage half the scenarios with a set of crisis checklists and the remaining scenarios from memory alone. The primary outcome measure was failure to adhere to critical processes of care. Participants were also surveyed regarding their perceptions of the usefulness and clinical relevance of the checklists. RESULTS: A total of 17 operating-room teams participated in 106 simulated surgical-crisis scenarios. Failure to adhere to lifesaving processes of care was less common during simulations when checklists were available (6% of steps missed when checklists were available vs. 23% when they were unavailable, P<0.001). The results were similar in a multivariate model that accounted for clustering within teams, with adjustment for institution, scenario, and learning and fatigue effects (adjusted relative risk, 0.28; 95% confidence interval, 0.18 to 0.42; P<0.001). Every team performed better when the crisis checklists were available than when they were not. A total of 97% of the participants reported that if one of these crises occurred while they were undergoing an operation, they would want the checklist used. CONCLUSIONS: In a high-fidelity simulation study, checklist use was associated with significant improvement in the management of operating-room crises. These findings suggest that checklists for use during operating-room crises have the potential to improve surgical care. (Funded by the Agency for Healthcare Research and Quality.).
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".