Current State of Point-of-care Ultrasound Usage in Canadian Emergency Departments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it