Training of Non-expert Users Using Remotely Delivered, Point-of-Care Tele-Ultrasound
Bibliographic record
Abstract
ABSTRACT: Many physicians, particularly those practicing in remote regions, lack training opportunities to develop point-of-care ultrasound (POCUS) skills. This pretest-posttest study quantified the skill improvement of learners after participating in a virtual training program that used real-time, remotely delivered point-of-care tele-ultrasound (tele-POCUS) for teaching and learner feedback provision. Ten physicians practicing in an urban tertiary (Kingston, Ontario, Canada, n = 6) or remote care center (Moose Factory, Ontario, Canada, n = 4) completed a 3-week educational program that consisted of e-learning module review, independent image acquisition practice, and expert-guided tele-POCUS consultations. Pretraining and posttraining assessments were performed to evaluate skill enhancement in image acquisition, image quality, and image interpretation for cardiac and lung/pleura POCUS using a 5-point Likert scale. A total of 76 tele-POCUS consultations were performed during the study period. Significant improvements in image quality were noted following remotely delivered mentorship and guidance (all P < 0.01). In cardiac POCUS, pretraining and posttraining comparisons noted significant improvements in image acquisition (means, 2.69-4.33; P < 0.02), quality (means, 2.40-4.03; P < 0.01), and interpretation (means, 2.50-4.40; P < 0.02). In lung/pleura POCUS, significant improvements in image acquisition (means, 3.00-4.43; P < 0.01), quality (means, 3.23-4.37; P < 0.01), and interpretation (means, 3.00-4.40; P < 0.01) were demonstrated. Introductory ultrasound can be taught to novice users using a virtual, live-streamed training format with tele-POCUS while demonstrating significant enhancement in imaging skills.
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 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 | 0.001 | 0.005 |
| 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) | 0.004 | 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".