Simulation training results in performance retention for the management of airway fires: A prospective observational study
Bibliographic record
Abstract
Given the severity of the consequences of operating room fires, it is recommended that every anaesthesiologist master fire safety protocols and periodically participate in operating room fire drills. The aim of the present study was to evaluate skill retention one year after an airway fire training programme. Anaesthesiology residents were evaluated using an airway fire simulation-based scenario one year after an educational programme that included a one-h long problem-based learning session, a simulation-based airway fire drill with debriefing, and a formal group discussion. The same simulation scenario was used for both the initial training and the one-year assessment. Thirty-eight anaesthesiology residents participated as pairs in the initial training programme. Of these, 36 participated in the evaluation a year later. Performance after one year was better than performance during the initial simulation. Time to removal of tracheal tube was 7.0 (4.0–12.8) s (median (interquartile range)) at the one-year assessment compared with 22.0 (18.5–52.5) s at the time of initial training ( P < 0.001). Performance improvement was also demonstrated by a higher incidence of performance of crucial action items (cessation of airway gases, removal of sponges and pouring of saline), as well as shorter duration of time necessary to perform these tasks. After controlling the fire, the time to re-establish ventilation by bag-mask ventilation or intubation was shorter at one year: 18.0 (11.0–29.0 ) s, compared with initial training 54.0 s (36.2–69.8) s ( P = 0.001). We conclude that skills are effectively retained for a year after an airway fire management training session.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".