Web‐Based Deliberate Practice of Pediatric Point‐of‐Care Ultrasound Cases in Resource‐Limited Settings
Bibliographic record
Abstract
OBJECTIVES: The main objective of this study was to implement an online pediatric case-based point-of-care ultrasound (POCUS) course in low-resource medical settings and examine learning outcomes and feasibility. METHODS: This was a multicenter prospective cohort study conducted in a convenience sample of clinicians affiliated with Médecins Sans Frontières (MSF) training sites. MSF POCUS trainers provided the standard hands-on, on-site POCUS training and supplemented this with access to a web-based course. Participants provided diagnoses for 400 image-based POCUS cases from four common pediatric POCUS applications until they achieved the mastery learning standard of 90% accuracy, sensitivity (cases with pathology), and specificity (cases without pathology). Each participant also completed a course evaluation. RESULTS: From 10 MSF sites, 110 clinicians completed 82,206 cases. There were significant learning gains across the POCUS applications with respect to accuracy (delta 14.2%; 95% CI 13.1, 15.2), sensitivity (delta 13.2%; 95% CI 12.1, 14.2), and specificity (delta 13.8%; 95% CI 12.7, 15.0). Furthermore, 90 (81.8%) achieved the mastery learning standard in at least one application, and 69 (62.7%) completed a course evaluation on at least one application for a total of 231 evaluations. Of these, 206 (89.2%) agreed/strongly agreed that the experience had relevance to their practice, met expectations, and had a positive user design. However, 59/110 (53.6%) clinicians reported a lack of protected time, and 54/110 (49.0%) identified challenges with accessing internet/hardware. CONCLUSIONS: In resource-limited MSF settings, implementing web-based POCUS case practice demonstrated successful learning outcomes despite approximately half of the participants encountering significant technical challenges.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".