The Current State of Advanced Pediatric Emergency Medicine Point-of-Care Ultrasound (POCUS) Training
Bibliographic record
Abstract
OBJECTIVES: This study aims to assess the current state of advanced pediatric emergency medicine (PEM) point-of-care ultrasound (POCUS) training in North America, including trends in dedicated PEM POCUS fellowships and alternative advanced POCUS training pathways, to better guide future educational efforts within the field. METHODS: We identified and surveyed 22 PEM POCUS fellowship directors across the United States and Canada regarding PEM POCUS fellowship application trends, potential barriers to pursuing additional POCUS training, and novel training models that meet the needs of the PEM POCUS workforce. RESULTS: The past 5 years have seen a growth in both PEM POCUS fellowship program number and trainee positions available, with a general impression by fellowship directors of a high demand for faculty who have these training credentials. However, there was a discordant drop in fellowship applicants and corresponding match rate in 2022, the cause of which is not clear. A number of programs are offering alternative advanced training options including combined PEM/POCUS fellowships and POCUS tracks within PEM fellowship. CONCLUSION: As POCUS use within PEM evolves, a growing number of advanced training options are being developed. Understanding the motivations and barriers for pursuing advanced POCUS training can help to shape these options going forward, to ensure the experience incorporated within each model meets the needs of trainees, the needs of PEM divisions, and the future needs of our field.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".