Virtual Supervision of Third Year Medical Students Using Handheld POCUS Devices and Cloud-based Image Archiving Provides Opportunity for Feedback and Skill Improvement
Bibliographic record
Abstract
Background: Feedback on Point of Care Ultrasound (POCUS) skills is essential for skill development. Providing feedback can be difficult in a large province with several distributed medical education sites. Use of handheld POCUS devices and a cloud-based image archiving enables virtual supervision. We evaluated the quality of uploaded images as well as feedback provided to students. Methods: Volunteer third year students were given access to handheld POCUS devices at various training sites. Students were encouraged to upload educational POCUS scans to their accounts where they would then receive feedback from faculty. Subsequently, images that met inclusion criteria were randomized and reviewed by a blinded expert using a global rating scale. Feedback was also analyzed. Finally, students completed a questionnaire on their technology-enhanced POCUS learning experience. Results: An independent-sampled t-test comparing mean ratings for initial images submitted prior to any feedback with those submitted after three rounds of feedback showed significant effect on image scores (2.60 vs 3.50, p = .040, d = .93). Feedback included 4 performance domains (indications, image generation, interpretation, and integration). Students found the technology easy to use and felt feedback was tailored to their learning needs. Conclusions: We observed that virtual feedback provided to medical students through a cloud-based work platform can be effective for enhancing POCUS skills.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".