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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".