High-fidelity simulation in neonatal resuscitation
Bibliographic record
Abstract
INTRODUCTION: There are currently few studies describing the use of high-fidelity (hi-fi) simulation in teaching neonatal resuscitation. Traditionally, residents are certified in the neonatal resuscitation program (NRP) after successful completion of a multiple-choice written examination and demonstration of skills during a hands-on 'mega-code'. In the present study, the use of a hi-fi simulation mannequin was compared with a standard plastic mannequin when teaching the megacode portion of the NRP. METHODS: In the present pilot study, 15 first-year residents were randomly assigned to demonstrate neonatal resuscitation knowledge, with either the hi-fi mannequin (SimBaby, Laerdal Medical Corporation, USA) or a traditional plastic mannequin (ALS Baby, Laerdal Medical Corporation, USA). A written evaluation was conducted before and after the intervention. Each pair of residents experienced the two scenarios. Video performance was then assessed and compared. RESULTS: Residents randomly assigned to the hi-fi mannequin rated the experience higher (31+/-3.3 versus 27+/-3.5; P=0.026), and required less redirection from instructors during the megacode (scenario 1: 4.5+/-1.7 versus 15+/-6.9; P=0.015 and scenario 2: 1.8+/-1.3 versus 9.3+/-2.5; P=0.0009) than those who were randomly assigned to the plastic mannequin. Residents randomly assigned to the hi-fi mannequin did not have improved written scores or improved intubation times. CONCLUSIONS: The present pilot study demonstrated that a hi-fi mannequin can be used as part of an educational program, such as the NRP. The use of this technology in neonatal resuscitation training is well-received by learners and may provide a more realistic model for training. Further work is required to clarify its role in task performance and team training.
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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 | 0.013 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".