Remote Mentoring of Point‐of‐Care Ultrasound Skills to Inexperienced Operators Using Multiple Telemedicine Platforms: Is a Cell Phone Good Enough?
Bibliographic record
Abstract
OBJECTIVES: Telemedicine technology contributes to the teaching of point-of-care ultrasound (US); however, expensive equipment can limit its deployment in resource-challenged settings. We assessed 3 low-cost telemedicine solutions capable of supporting remote US training to determine feasibility, acceptability, and effectiveness. We also explored the value of instructional videos immediately before telementoring. METHODS: Thirty-six participants were randomly assigned to receive US mentoring in 1 of 3 telemedicine conditions: multiple fixed cameras, a smartphone, and traditional audio with a live US stream. Participants were then asked to perform a standardized US examination of the right upper quadrant under remote guidance. We measured observer's global ratings of performance along with the mentor's and student's rating of effort and satisfaction to determine which of the 3 approaches was most feasible, acceptable, and effective. During the second phase, students were randomized to watch an instructional video or not before receiving remote coaching on how to complete a subxiphoid cardiac examination. Effort, satisfaction, and performance from the independent observer's and student's perspective were surveyed. RESULTS: There was no significant difference between the different telemedicine setups from the observer's perspective; however, the mentor rated the smartphone significantly worse (P = .028-.04) than other technologies. Platforms were rated equivalent from the student's perspective. No benefit was detected for watching an instructional video before the mentored task. CONCLUSIONS: Remote US skills can be taught equally effectively by using a variety of telemedicine technologies. Smartphones represent a viable option for US training in resource-challenged settings.
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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 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".