Training Nonexpert Users in Cardiopulmonary Point-of-Care Ultrasound Using a Virtual Curriculum and a Teleconsultation Model: A Multicentre Study
Bibliographic record
Abstract
Background Disparities in access to post-graduate cardiopulmonary point-of-care ultrasound (POCUS) training have limited uptake by non-specialists in remote care centres. This multicentre pre-post study evaluated the skill improvement of learners after participating in a longitudinal and virtual POCUS training program. Methods Non-expert POCUS users were recruited at urban teaching hospitals and geographically remote hospitals/nursing stations across four Canadian provinces. The three-week educational program consisted of e-learning, independent imaging practice, and point-of-care tele-ultrasound (tele-POCUS) consultations with experts during clinical encounters. Standardized assessments were used to evaluate skill improvement in image acquisition, image quality, and image interpretation for cardiac and lung/pleura POCUS (as measured on a 5-point Likert scale) after program completion and remotely delivered guidance via tele-POCUS. Results Among 29 learners, 17 (41% female) completed the training program, of which 7 practiced in remote hospitals/nursing stations. For cardiac POCUS, pre- and post-training assessments noted improvements in image acquisition (mean scores: 3.02 to 4.48, p<0.01), quality (2.49 to 4.06, p<0.01), and interpretation (3.03 to 4.44, p<0.01). Improvements in image acquisition (3.27 to 4.63, p<0.01), quality (3.25 to 4.53, p<0.01), and interpretation (3.35 to 4.65, p<0.01) were also noted for lung/pleura POCUS. A total of 153 tele-POCUS consultations (77 cardiac and 76 lung/pleura) were performed. Image acquisition improved after remote guidance was provided to learners using tele-POCUS (all p<0.01). Results were similar in analyses stratified by geographical setting. Conclusion Cardiopulmonary POCUS can be successfully taught to learners in diverse geographical settings using a virtual training format and tele-POCUS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".