Improving Pediatric Patients’ Magnetic Resonance Imaging Experience With an In-Bore Solution: Design and Usability Study
Bibliographic record
Abstract
BACKGROUND: Annually, millions of children undergo a magnetic resonance imaging (MRI) examination. Hospitals increasingly aim to scan young children awake, as doing so benefits both patients and health care systems. To help hospitals reduce the need for anesthesia, we have developed solutions to prepare pediatric patients at home and in the hospital. OBJECTIVE: The goal of our project was to design, develop, and test a solution that extends our preparation solutions by guiding and engaging children during their MRI examination. METHODS: Pediatric In-bore was designed to deliver a familiar experience by reusing design elements from our preparation solutions. It offers child-friendly movies and auditory and visual guidance about examination progress and breath holding. To evaluate children's liking and understanding of the solution, we conducted a usability study. Ten healthy children participated in a mock MRI examination featuring pediatric In-bore. We observed task compliance (ability to lie still and hold one's breath) and conducted guided interviews to assess their experience and understanding of the guidance offered. RESULTS: Participants (aged 5 to 10 years) were generally positive about pediatric In-bore. They liked the main character (Ollie the elephant) and her movie. Auditory and visual guidance were generally liked and understood. All but one participant successfully managed to lie still during the mock examination, and 6 (60%) out of 10 participants successfully held their breath. CONCLUSIONS: Pediatric In-bore appears promising for engaging and guiding young children during awake MRI. It completes the Pediatric Coaching solution that now offers guidance throughout the MRI journey. Future research can expand on this work by evaluating the clinical impact of the Pediatric Coaching solution in a larger and more diverse sample of pediatric patients.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".