Utilizing Simulation to Identify Latent Safety Threats During Neonatal Magnetic Resonance Imaging Procedure
Bibliographic record
Abstract
INTRODUCTION: Transfer of infants for magnetic resonance imaging (MRI) from the neonatal intensive care unit (NICU) requires exposure to unfamiliar environments and involve multiple complex human and system interactions, which can compromise patient safety. In situ simulation (ISS) offers an opportunity to identify latent safety threats (LSTs) that may occur during this high-risk procedure. Our primary aim was to use ISS to identify modifiable LSTs during the MRI procedure: involving neonatal transport to/from NICU to the MRI and the MRI scan. Secondarily, we compared the overall performance and needs of specialized versus nonspecialized transport personnel. METHODS: In situ simulations of the MRI procedure (transport and scan) were performed for 9 months involving specialized and nonspecialized transport personnel. Two simulation scenarios were used, one involving an intubated infant and one nonintubated infant. After each simulation, participants underwent a standardized debriefing and answered questionnaires on safety threats and team function. The results were then used to identify and implement mitigation strategies. RESULTS: Among 10 simulations completed, 7 were by specialized and 3 by nonspecialized teams. In total, 116 LSTs were identified (22 involving medication, 12 equipment, and 82 resources/system issues). Preprocedure deliberation with anticipation/preparedness for patient deterioration, and the need for clinical checklists and protocols were identified as important requirements. After completion of the project, protocols (ie, sedation), checklists (ie, pretransport), and policies (ie, environmental orientation) were adapted to address the gaps. CONCLUSIONS: In situ simulations were able to identify important safety risks during transport of neonatal patients from the NICU to the MRI suite, informing changes in MRI transport policy.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".