High-fidelity simulation in neonatal resuscitation
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it