Can Untrained Patients Perform Their Own Skin and Soft Tissue Ultrasound Examination by Teleguidance?
Bibliographic record
Abstract
Objectives: This pilot study aims to determine if patients untrained in performing ultrasound can self-scan to obtain images under remote clinician teleguidance during a simulated telehealth encounter. This study also seeks to describe the patients’ comfort level and barriers to performing an ultrasound examination on themselves using a handheld ultrasound device. Methods: This was a single center prospective observational cohort study conducted over a 4-month period in 2021. Patients were eligible if they had no prior training in the use of ultrasound and in the use of teleguidance. They voluntarily consented to participate at a single ambulatory internal medicine clinic. Results: 20 participants were enrolled and underwent teleguidance to ultrasound their own skin and soft tissues at the antecubital fossae. Six second video clips were evaluated by 2 subject matter experts using the Point of Care Ultrasound Image Quality scale. A score >7 was considered adequate for diagnostic interpretation. The average score was 10.15/14, with a minimum score of 5/14, and maximum score of 14/14 and a standard deviation (SD) of 2.39 using a two tailed Z-score. Setting alpha at 0.05 the 95% CI was (5.47-14.83). Conclusion: In a pilot study of 20 participants with no ultrasound experience, untrained healthy volunteers were able to perform technically acceptable and interpretable ultrasound scans using teleguidance by a trained clinician.
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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 | 0.010 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".