Ultrasound credentialing in North American emergency department systems with ultrasound fellowships: a cross-sectional survey
Bibliographic record
Abstract
OBJECTIVE: To describe the credentialing systems of North American emergency department systems (EDS) with emergency ultrasound (EUS) fellowship programmes. METHODS: This is a prospective, cross-sectional, survey-based study of North American EUS fellowships using a 62-item, pilot-tested, web-based survey instrument assessing credentialing and training systems. The American College of Emergency Physicians (ACEP) distributed the surveys using SNAP survey (Snap Surveys Ltd, Portsmouth, New Hampshire, USA). RESULTS: Over 6 months, 75 eligible programmes were surveyed, 55 responded (73% response rate); 1 declined to participate leaving 54 participating programmes. Less than 20% of EDS credential nurses, physician assistants, nurse practitioners and students in EUS. Respondent EDS reported having an average of 4.2 ± 3.3 ultrasound faculty members (faculty identifying their career focus as EUS). The median number of annual point-of-care ultrasounds reported was 5000 (IQR 3000-8000). 30 EDS (56%) credential each examination individually and 48 EDS (89%) use ACEP credentialing criteria. 61% of fellowship leadership believe their credentialing system is either satisfactory or very satisfactory (Cronbach's coefficient α=0.84). CONCLUSIONS: The data show heterogeneity among North American EDS with EUS fellowship programmes with regard to credentialing systems despite published guidelines from the ACEP and Canadian Emergency Ultrasound Society.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".