Current State of Point-of-care Ultrasound Usage in Canadian Emergency Departments
Bibliographic record
Abstract
Background Point-of-care ultrasound (POCUS) has many applications in emergency medicine, which have been proven to improve patient outcomes. Training programs and well-established guidelines for its use are available, but Canadian adoption rates and attitudes toward this technology have not been recently assessed. Objectives This study aimed to provide a national assessment of the current use of POCUS in Canadian emergency departments (ED) including patterns of use, attitudes towards its role, descriptors of training experience, as well as barriers to increased utilization. Methods An electronic survey was sent to physician members of the Canadian Association of Emergency Physicians. The survey included questions related to demographics, attitudes towards POCUS, POCUS utilization, and barriers to POCUS use. Responses were statistically analyzed to identify significant associations. Results Responses demonstrated a strong association between POCUS training and amount of POCUS usage. Neither hospital type nor community type was associated with the degree of POCUS usage. POCUS was most widely adopted for Canadian Point of Care Ultrasound Society (CPOCUS) core applications and has increased since the last national survey. The most commonly reported barrier to increased POCUS adoption was the lack of training. Most physicians have formal POCUS training in core applications, and approximately one third have advanced training. Conclusions POCUS training and utilization appear to have increased since the last national assessment. This provides a foundation for future POCUS research.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".