Development of a novel pediatric point‐of‐care ultrasound question bank using a modified Delphi process
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Measuring pediatric emergency medicine (PEM) fellow competency in point-of-care ultrasound (POCUS) is important for ensuring adequate training and performance. Assessment may include direct observation, image review, quality assessment, and written examination. The purpose of this study was to develop a pediatric POCUS question bank that could subsequently be used as a POCUS assessment for graduating PEM fellows. METHODS: We organized a 10-person question writing group (QWG). Eight hold expertise in POCUS and two hold expertise in medical education. Members of the QWG created questions within four domains: interpretation/diagnosis (50% of questions), anatomy (30%), physics (10%), and pitfalls (10%). POCUS faculty ascertained content validity and the medical education faculty revised questions for syntax and readability. In 2016, we recruited 31 pediatric POCUS experts. The majority were members of the P2 Network, an international group of experts and leaders in PEM POCUS, to participate in three iterative rounds of a modified Delphi process to review, revise, and establish consensus on the question bank. RESULTS: = 47) in the final round. The final question bank included 393 questions covering 17 pediatric POCUS applications. CONCLUSION: We developed a 393-question bank to aid in the assessment of PEM POCUS competency. Future work includes piloting the questions with PEM fellows to evaluate the response process and implementing the assessment tool to establish a minimum passing score.
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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 | 0.158 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".