Impact of Critical Event Checklists on Anaesthetist Performance in Simulated Operating Theatre Emergencies
Bibliographic record
Abstract
Introduction Crises in the operating theatre during a paediatric case are rare with the incidence of anesthesia-related cardiac arrest in non-cardiac patients being 1.4/10,000. In order to address this, the Society for Pediatric Anesthesia (SPA) developed cognitive aids (CAs) in the form of Critical Event Checklists (SPA CECs). Several studies have demonstrated the benefit of CAs in improving performance of critical tasks. Despite the presence of CAs, individuals often do not use the aids consistently. The objective of our study was to investigate whether the presence of SPA CECs, and orientation to these tools, improve the performance of trainees during simulated critical events. Methods With local Research Ethics Board (REB) approval we used a randomized, 2 x 2 factorial design. The first randomization was the participant orientation to the SPA CECs (e-module vs. didactic). The second randomization assigned participants to complete the simulations with or without SPA CECs available. The simulations were videoed and rated by two raters using a scenario-specific checklist and global rating scale (GRS). Results We conducted 78 simulations. The SPA CEC was used in 17.9% of scenarios. The SPA CEC was used in 44.8% of diagnosis-based scenarios and only 2.0% of generic problem-based scenarios. Participants' performance was superior with the SPA CEC present (GRS mean 3 [SD 1.27]) than without the SPA CEC available (GRS mean 2.43 [SD 0.89]) (p = 0.048). Conclusion Overall, we showed that uptake of the SPA CECs is poor. We also demonstrated that when the SPA CECs are utilized, they enhance the performance of trainees in simulated operating room (OR) critical events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".