Effect of simulation‐based team training in airway management: a systematic review
Bibliographic record
Abstract
Major complications associated with airway management are rare but often have serious consequences. Complications frequently result from failures in communication and teamwork. We performed a systematic review on the effect of simulation-based team training on patient outcomes, healthcare professionals' clinical performance and preparedness for airway management. We included studies with simulation-based team training in airway management as the educational intervention, using any comparator, outcome and design. Two authors independently selected articles and assessed risk of bias using the Medical Education Research Study Quality Instrument and Newcastle-Ottawa Scale-Education. We screened 1248 titles and evaluated 116 full-text articles. Twenty-two studies were included. The Kirkpatrick model for evaluation of training was used to organise outcomes. Four studies reported patient-centred outcomes (Kirkpatrick level 4), and three studies' outcomes related to healthcare professionals' clinical performance (Kirkpatrick level 3). The results were ambiguous and the studies had significant methodological limitations, making it difficult to draw conclusions on the effect of simulation-based team training. To describe preparedness for airway management, we used outcomes related to participants' attitudes or perceptions and outcomes related to knowledge or skills demonstrated in a test setting (Kirkpatrick level 2). Most studies reporting these outcomes were in favour of simulation-based team training, but were prone to bias. We consider the current evidence to be weak and recommend that future research should be based on randomised study designs and patient-centred outcomes.
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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 | 0.012 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".