Virtual guidance: a new technique to empower point-of-care ultrasound in remote or extreme environments
Bibliographic record
Abstract
Abstract Purpose Remote guidance techniques have been developed by NASA researchers to allow non-clinicians to perform complex ultrasound examinations on the International Space Station to increase clinical diagnostic capabilities. Real-time or near real-time communication will not be an option for missions beyond the Earth and Moon; non-experts will have to scan autonomously. We investigated the ability of non-experts to perform point-of-care ultrasound in a remote location using “virtual guidance”, consisting of a video-based training and troubleshooting guide to acquire cardiac ultrasound images. Methods Non-expert operators (n = 4) reviewed a short (<15 min) cardiac ultrasound examination training video using dedicated video glasses and an iPod. They then acquired echocardiography scansets on normal, volunteer subjects at Resolute Bay, Canada using a portable ultrasound device. Image quality was evaluated using a scoring system by two experts in echocardiography. Results Cardiac ultrasound examinations were autonomously completed by four non-expert operators using virtual guidance in under 30 min and judged to be adequate for clinical interpretation. Virtual guidance with the video glasses and streaming examination guide was accepted by all operators as an effective guidance technique for this purpose. Conclusions Virtual guidance is a novel technique that may allow data acquisition by non-expert operators autonomously when on-site expertise or real time support is not available. Further refinement of the technique should be explored to enhance autonomous medical capabilities in isolated or underserved settings, either on or off the planet.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".