Non-technical skills of surgeons and anaesthetists in simulated operating theatre crises
Bibliographic record
Abstract
BACKGROUND: Deficiencies in non-technical skills (NTS) have been increasingly implicated in avoidable operating theatre errors. Accordingly, this study sought to characterize the impact of surgeon and anaesthetist non-technical skills on time to crisis resolution in a simulated operating theatre. METHODS: Non-technical skills were assessed during 26 simulated crises (haemorrhage and airway emergency) performed by surgical teams. Teams consisted of surgeons, anaesthetists and nurses. Behaviour was assessed by four trained raters using the Non-Technical Skills for Surgeons (NOTSS) and Anaesthetists' Non-Technical Skills (ANTS) rating scales before and during the crisis phase of each scenario. The primary endpoint was time to crisis resolution; secondary endpoints included NTS scores before and during the crisis. A cross-classified linear mixed-effects model was used for the final analysis. RESULTS: Thirteen different surgical teams were assessed. Higher NTS ratings resulted in significantly faster crisis resolution. For anaesthetists, every 1-point increase in ANTS score was associated with a decrease of 53·50 (95 per cent c.i. 31·13 to 75·87) s in time to crisis resolution (P < 0·001). Similarly, for surgeons, every 1-point increase in NOTSS score was associated with a decrease of 64·81 (26·01 to 103·60) s in time to crisis resolution in the haemorrhage scenario (P = 0·001); however, this did not apply to the difficult airway scenario. Non-technical skills scores were lower during the crisis phase of the scenarios than those measured before the crisis for both surgeons and anaesthetists. CONCLUSION: A higher level of NTS of surgeons and anaesthetists led to quicker crisis resolution in a simulated operating theatre environment.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".