158: Present and Future of Emergency Point-of-Care Ultrasound in Pediatric Emergency Fellowship Programs in Canada
Bibliographic record
Abstract
Pediatric Emergency Point-of-care Ultrasound (POCUS) has become more and more important in the emergent care of ill and injured children over the last years. No information is currently available about its adoption and integration into Canadian pediatric emergency medicine (PEM) fellowship programs. The objective of this study was to describe current state as well as requests for POCUS training offered by Canadian PEM fellowship programs as perceived by both program directors and actively training fellows. Two web-based surveys were created by the survey authors: one for fellowship program directors and one for actively training fellows. The survey was tested and refined, and posted on a web-based survey site. The links for both questionnaires were distributed to all PEM fellowship program directors in Canada for dissemination to their fellows. A modified Dillman's method was used to maximize survey responses. Survey questions explored: current use of POCUS clinically at the training sites, existing POCUS curriculum and training opportunities, and interest in future curriculum development. A total of 9/10 (90%) fellowship program directors as well as 42/60 (70%) fellows responded. Currently a formal curriculum in POCUS is established in five of nine PEM programs. Most fellows (83%) had no training in POCUS before their fellowship but 74% reported a formal training during fellowship (FAST and focused cardiac examination mainly). Only 33% of fellows reported specific pediatric POCUS training. 50% of fellows stated using POCUS at least once a week, while directors indicated that the majority of faculty rarely uses POCUS (70%) in clinical practice. Main application in both groups is the FAST exam. For fellows and fellowship directors the major barrier in learning POCUS was the lack of trained faculty (95% and 90%, respectively), followed by faculty time, and interest in the technology. Finally, 86% of fellows described training in POCUS as very important or essential and they expressed their need for more training. While demand from PEM fellows is high, there is a great variability in implantation of POCUS training in Canadian PEM programs. The most important barrier in POCUS training is related to the lack of trained faculty, a fact that needs to be addressed by program directors. Canadian PEM programs should develop a conjoint standardized POCUS curriculum.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".