Experience with Emergency Ultrasound Training by Canadian Emergency Medicine Residents
Bibliographic record
Abstract
INTRODUCTION: Starting in 2008, emergency ultrasound (EUS) was introduced as a core competency to the Royal College of Physicians and Surgeons of Canada (Royal College) emergency medicine (EM) training standards. The Royal College accredits postgraduate EM specialty training in Canada through 5-year residency programs. The objective of this study is to describe both the current experience with and the perceptions of EUS by Canadian Royal College EM senior residents. METHODS: This was a web-based survey conducted from January to March 2011 of all 39 Canadian Royal College postgraduate fifth-year (PGY-5) EM residents. Main outcome measures were characteristics of EUS training and perceptions of EUS. RESULTS: Survey response rate was 95% (37/39). EUS was part of the formal residency curriculum for 86% of respondents (32/37). Residents most commonly received training in focused assessment with sonography for trauma, intrauterine pregnancy, abdominal aortic aneurysm, cardiac, and procedural guidance. Although the most commonly provided instructional material (86% [32/37]) was an ultrasound course, 73% (27/37) of residents used educational resources outside of residency training to supplement their ultrasound knowledge. Most residents (95% [35/37]) made clinical decisions and patient dispositions based on their EUS interpretation without a consultative study by radiology. Residents had very favorable perceptions and opinions of EUS. CONCLUSION: EUS training in Royal College EM programs was prevalent and perceived favorably by residents, but there was heterogeneity in resident training and practice of EUS. This suggests variability in both the level and quality of EUS training in Canadian Royal College EM residency programs. [West J Emerg Med. 2014;15(3):306-311.].
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".