Nontechnical Skills in Anesthesia Crisis Management with Repeated Exposure to Simulation-based Education
Bibliographic record
Abstract
BACKGROUND: Critical incident reporting and observational studies have identified nontechnical skills that are vital to successful anesthesia crisis management. Examples of such skills include task management, team working, situation awareness, and decision making. These skills are not necessarily acquired through clinical experience and may need to be specifically taught. This study uses a high-fidelity patient simulator to assess the effect of repeated exposure to simulated anesthesia crises on the nontechnical skills of anesthesia residents. METHODS: After institutional research board approval and informed consent, 20 anesthesia residents were recruited. Each resident was randomized to participate as the primary anesthesiologist in the management of three different simulated anesthesia crises using a high-fidelity patient simulator. After each session, videotaped footage was used to facilitate debriefing of their nontechnical skills. The videotapes were later reviewed by two expert blinded independent assessors who rated each resident's nontechnical skills by using a previously validated and reliable marking system. RESULTS: : A significant improvement in the nontechnical skills of residents was demonstrated from their first to second session and from their first to third session (both P < 0.005). However from their second to third session, no significant improvement was observed. Interrater reliability between assessors was modest (single rater intraclass correlation = 0.53). CONCLUSION: A single exposure to anesthesia crises using a high-fidelity patient simulator can improve the nontechnical skills of anesthesia residents. However, an additional simulation session may confer little or no additional benefit.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| 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".