The Variable Journey in Learning to Interpret Pediatric Point‐of‐care Ultrasound Images: A Multicenter Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: To complement bedside learning of point-of-care ultrasound (POCUS), we developed an online learning assessment platform for the visual interpretation component of this skill. This study examined the amount and rate of skill acquisition in POCUS image interpretation in a cohort of pediatric emergency medicine (PEM) physician learners. METHODS: This was a multicenter prospective cohort study. PEM physicians learned POCUS using a computer-based image repository and learning assessment system that allowed participants to deliberately practice image interpretation of 400 images from four pediatric POCUS applications (soft tissue, lung, cardiac, and focused assessment sonography for trauma [FAST]). Participants completed at least one application (100 cases) over a 4-week period. RESULTS: We enrolled 172 PEM physicians (114 attendings, 65 fellows). The increase in accuracy from the initial to final 25 cases was 11.6%, 9.8%, 7.4%, and 8.6% for soft tissue, lung, cardiac, and FAST, respectively. For all applications, the average learners (50th percentile) required 0 to 45, 25 to 97, 66 to 175, and 141 to 290 cases to reach 80, 85, 90, and 95% accuracy, respectively. The least efficient (95th percentile) learners required 60 to 288, 109 to 456, 160 to 666, and 243 to 1040 cases to reach these same accuracy benchmarks. Generally, the soft tissue application required participants to complete the least number of cases to reach a given proficiency level, while the cardiac application required the most. CONCLUSIONS: Deliberate practice of pediatric POCUS image cases using an online learning and assessment platform may lead to skill improvement in POCUS image interpretation. Importantly, there was a highly variable rate of achievement across learners and applications. These data inform our understanding of POCUS image interpretation skill development and could complement bedside learning and performance assessments.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".