Simulation‐Based Structured Education Supports Focused Neonatal Cranial Ultrasound Training
Bibliographic record
Abstract
OBJECTIVES: Brain injury in preterm neonates may cause clinical deterioration and requires timeous bedside diagnosis. Teaching cranial ultrasound (US) skills using fragile preterm neonates is challenging. The purpose of this study was to test the effectiveness and feasibility of using task-trainer computer-based simulators and US-suitable cranial phantoms in combination with teaching sessions in teaching novices to perform focused cranial US evaluations for identifying substantial intraventricular hemorrhage. METHODS: This was a prospective interventional educational study targeting participants with no prior skills in neonatal cranial US. Participants attended a 2-day training workshop, with didactic and hands-on interactive sessions using computer-based and 3-dimensional printed phantom simulators. Participants then performed a cranial US scan on a healthy neonate to assess the diagnostic quality of the images acquired. Individual precourse and postcourse knowledge tests were compared. To test recall, participants also submitted US images acquired on neonates within 3 and 6 months of attending the course. RESULTS: Forty-five participants completed the training modules. Mean knowledge scores increased significantly (in brain anatomy, brain physiology, intracranial disorders, and US physics domains). Thirty-eight cranial US scans were acquired during the course, 22 within 3 months after completion, and 34 within 6 months after completion. Thirty-two (84%) of the initial 38 case images, 17 (77%) of 22 images submitted within 3 months, and 32 (94%) of 34 images submitted within 6 months after course completion were of diagnostic quality. CONCLUSIONS: A structured training module with didactic and hand-on training sessions using simulators and phantoms is feasible and supports training of clinicians to perform focused cranial US examinations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".