Simulation-Based Emergency Team Training in Pediatrics: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: The rare event of handling critically ill children often challenge the emergency care team. Several studies have investigated effects of simulation-based team training to prepare for such events, but the body of evidence remains to be compiled. We performed a systematic review of the effects of simulation-based team training on clinical performance and patient outcome. METHODS: From a search of MEDLINE, Embase, CINAHL, and Cochrane Library, we included studies of team training in emergency pediatric settings with reported clinical performance and patient outcomes. We extracted data using a predefined template and assessed risk of bias using the Cochrane risk-of-bias tool for randomized trials 2.0 and the Newcastle Ottawa Quality Assessment Scale. RESULTS: We screened 1926 abstracts and included 79 studies. We identified 15 studies reporting clinical health care professional performance or patient outcomes. Four studies reported survival data, 5 reported time-critical clinical events, 5 reported adherence to guidelines, checklists or tasks, and 2 reported on airway management. Randomized studies revealed improved team performance in simulated reevaluations 2 to 6 months after intervention. A meta-analysis was impossible because of heterogeneous interventions and outcomes. Most included studies had significant methodological limitations. CONCLUSIONS: Pediatric simulation-based team training improves clinical performance in time-critical tasks and adherence to guidelines. Improved survival was indicated but not concluded because of high risk of bias. Team performance and technical skills improved for at least 2 to 6 months. Future research should include longer-term measures of skill retention and patient outcomes or clinical measures of treatment quality whenever possible.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| 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".