Teaching Ultrasound at the Point of Care in Times of Social Distancing
Bibliographic record
Abstract
Point-of-care ultrasound has become an integral aspect of critical care training. The Bedside Assessment by Sonography In Critical Care Medicine Curriculum was established at the University of Toronto to train critical care trainees in basic echocardiography and general critical care ultrasound. During the coronavirus disease (COVID-19) pandemic, our program needed to adapt quickly to ensure staff safety and adherence to infection-control protocols. In this article, we share our experience and reflect on the challenges and benefits of shifting from a primarily in-person teaching model to a hybrid model of remote and in-person teaching. Curricular changes were threefold: the transition to entirely web-based interactive didactic teaching and online imaging interpretation modules, the recruitment of sonographers at multiple academic sites as instructors to facilitate in-person practices with lower instructor to trainee ratio, and the use of a mobile application for informal group case-based discussions. Challenges included lost opportunities for scanning healthy volunteers, variability in attendance at online lectures, and a lower number of study submissions for review. However, curricular changes enabled maintenance of directly observed practice, high levels of engagement with recorded content, and an expansion of our reach to a global audience. We believe that future curricula should combine high-quality online curriculum and resources with the ongoing in-person delivery of key elements of curriculum to allow for direct observation and feedback as well as the maintenance of self-directed point-of-care ultrasound portfolios.
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 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 | 0.002 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".