Quality Assurance for Point-of-Care Ultrasound in North American Pediatric Emergency Medicine Fellowships
Bibliographic record
Abstract
OBJECTIVES: The American Academy of Pediatrics, the Society for Academic Emergency Medicine, and the American College of Emergency Physicians released a policy statement endorsing the use of point-of-care ultrasound (POCUS) by pediatric emergency medicine (PEM) providers. This statement specifically recommends that emergency departments have a credentialing and quality assurance (QA) program for POCUS. There is limited knowledge of how QA for POCUS is currently carried out in pediatric emergency departments with PEM training programs. METHODS: We sent a cross-sectional web-based survey to all 81 PEM fellowship-training programs in the United States and Canada between June 2016 and June 2017. RESULTS: Sixty-six of 81 programs (81.2%) responded. Sixty-five percent of responding PEM training programs had POCUS-trained faculty or a POCUS champion at their institution. Forty-six percent had a POCUS fellowship in their institution, with 10 programs having PEM-specific POCUS fellowships. Programs with POCUS fellowships were more likely to save all images, review all scans, review scans more frequently, provide feedback, and bill compared with programs without POCUS fellowships. CONCLUSIONS: Point-of-care ultrasound is growing in PEM fellowship-training programs, with a majority of programs now having faculty members trained or interested specifically in POCUS. Most programs prefer more frequent and thorough QA processes, and programs with POCUS fellowships are more likely to have more frequent and thorough QA processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.107 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".