The impact of critical event checklists on medical management and teamwork during simulated crises in a surgical daycare facility
Bibliographic record
Abstract
Although the incidence of major adverse events in surgical daycare centres is low, these critical events may not be managed optimally due to the absence of resources that exist in larger hospitals. We aimed to study the impact of operating theatre critical event checklists on medical management and teamwork during whole-team operating theatre crisis simulations staged in a surgical daycare facility. We studied 56 simulation encounters (without and with a checklist available) divided between an initial session and then a retention session several months later. Medical management and teamwork were quantified via percentage adherence to key processes and the Team Emergency Assessment Measure, respectively. In the initial session, medical management was not improved by the presence of a checklist (56% without checklist vs. 62% with checklist; p = 0.50). In the retention session, teams performed significantly worse without the checklists (36% without checklist vs. 60% with checklist; p = 0.04). We did not observe a change in non-technical skills in the presence of a checklist in either the initial or retention sessions (68% without checklist vs. 69% with checklist (p = 0.94) and 69% without checklist vs. 65% with checklist (p = 0.36), respectively). Critical events checklists do not improve medical management or teamwork during simulated operating theatre crises in an ambulatory surgical daycare setting.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".