Teaching emergency ultrasound to emergency medicine residents: a scoping review of structured training methods
Bibliographic record
Abstract
BACKGROUND: Over the past 2 decades, emergency ultrasound has become essential to patient care, and is a mandated competency for emergency medicine residency graduation. However, the best evidence regarding emergency ultrasound education in residency training is not known. We performed a scoping review to determine the (1) characteristics and (2) outcomes of published structured training methods, (3) the quality of publications, and (4) the implications for research and training. METHODS: We searched broadly on multiple electronic databases and screened studies from the United States and Canada describing structured emergency ultrasound training methods for emergency medicine residents. We evaluated methodological quality with the Medical Education Research Study Quality Instrument (MERSQI), and qualitatively summarized study and intervention characteristics. RESULTS: A total of 109 studies were selected from 6712 identified publications. Publications mainly reported 1 group pretest-posttest interventions (38%) conducted at a single institution (83%), training in image acquisition (82%) and interpretation (94%) domains with assessment of knowledge (44%) and skill (77%) outcomes, and training in cardiac (18%) or vascular access (15%) applications. Innovative strategies, such as gamification, cadaver models, and hand motion assessment are described. The MERSQI scores of 48 articles ranged from 0 to 15.5 (median, 11.5; interquartile range, 9.6-13.0) out of 18. Low scores reflected the absence of reported valid assessment tools (73%) and higher level outcomes (90%). CONCLUSIONS: Although innovative strategies are illustrated, the overall quality of research could be improved. The use of standardized planning and assessment tools, intentionally mapped to targeted domains and outcomes, might provide valuable formative and summative information to optimize emergency ultrasound research and training.
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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 | 0.041 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".