Self-administered at-home lung ultrasound with remote guidance in patients without clinical training
Bibliographic record
Abstract
BACKGROUND: Access to timely and accurate diagnostic imaging is essential for high-quality healthcare. Point-of-care ultrasound has been shown to be accessible and effective in many aspects of healthcare, including assessing changes in lung pathology. However, few studies have examined self-administered at-home lung ultrasound (SAAH-LUS), in particular performed by non-clinical patients (NCPs). RESEARCH QUESTION: Are NCPs able to perform SAAH-LUS using remote teleguidance and produce interpretable images? STUDY DESIGN: Patients were enrolled to the study in a mix of in-person and virtual recruitment, and shipped a smartphone as well as a point of care ultrasound device. Tele-guidance was provided by a remote physician using software integrated with the point of care ultrasound device, allowing real-time remote visualization and guidance of a patient scanning their own chest. A post-intervention survey was conducted to assess patient satisfaction, feasibility, and acceptability of SAAH-LUS. Two POCUS expert reviewers reviewed the scans for interpretability, and inter-rater agreement between the two reviewers was also computed. RESULTS: Eighteen patients successfully underwent 7-14 days of daily telemedicine in parallel to daily SAAH-LUS. Across 1339 scans obtained from ten different lung zones, the average proportion of interpretability was 96% with a chance-corrected agreement, or Cohen's kappa, reported as κ = 0.67 (significant agreement). 100% of NCPs surveyed found SAAH-LUS to be a positive experience, particularly for its ease of operation and ability to increase access to healthcare services. INTERPRETATION: This study demonstrates that NCPs can obtain interpretable LUS images at home, highlighting the potential for SAAH-LUS to increase diagnostic capacity, particularly for rural and remote regions where complex imaging and healthcare providers are difficult to obtain. Trial registration The clinical trials has been registered (clinicaltrials.gov). REGISTRATION NUMBER: NCT04967729.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".