Design of a point‐of‐care ultrasound curriculum for pediatric emergency medicine fellows: A Delphi study
Bibliographic record
Abstract
OBJECTIVES: There has been a steady increase in the growth and utilization of point-of-care ultrasound (POCUS) in pediatric emergency medicine (PEM). POCUS has been established as an Accreditation Council for Graduate Medical Education (ACGME) core requirement for accreditation of PEM fellowship programs. Despite this requirement, training guidelines regarding POCUS knowledge and skills have yet to be developed. The purpose of this project was to develop a curriculum and a competency checklist for PEM fellow POCUS education. METHODS: We formed a core leadership group based on expertise in one or more key areas: PEM, POCUS, curriculum development, or Delphi methods. We recruited 29 PEM POCUS or ultrasound education experts from North America to participate in a three-round electronic Delphi project. The first Delphi round asked experts to generate a list of the core POCUS knowledge and skills that a PEM fellow would need during training to function as an autonomous practitioner. Subsequent rounds prioritized the list of knowledge and skills, and the core leadership group organized knowledge and skills into global competencies and subcompetencies. RESULTS: The first Delphi round yielded 61 POCUS areas of knowledge and skills considered important for PEM fellow learning. After two subsequent Delphi rounds, the list of POCUS knowledge and skills was narrowed to 38 items that addressed elements of six global competencies. The core leadership group then revised items into subcompetencies and categorized them under global competencies, developing a curriculum that defined the scope (depth of content) and sequence (order of teaching) of these POCUS knowledge and skill items. CONCLUSIONS: This expert, consensus-generated POCUS curriculum provides detailed guidance for PEM fellowships to incorporate POCUS education into their programs. Our curriculum also identifies core ultrasound knowledge and skills needed by PEM fellows to perform the specific POCUS applications recommended in prior publications.
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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 | 0.130 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".