Comparative Effectiveness of Real-Time Teleultrasound Versus In-Person Ultrasound
Bibliographic record
Abstract
What Is the Issue? Ultrasound imaging requires highly trained professionals for accurate diagnostic exams and interpretation. Ultrasound is more affordable and portable than CT and MRI and does not expose patients to radiation. This makes ultrasound the preferred method for real-time assessment and soft tissue imaging. For more detailed or complex imaging, or when clinically indicated, CT and MRI may be more appropriate. In Canada, less than 28% of rural hospitals have in-house access to ultrasound, leading to patient transfers. Ultrasound exams are often conducted by sonographers, and there is a notable shortage of sonographers both in Canada and worldwide. Limited access to skilled ultrasound professionals has led to the development of teleultrasound (TUS), which supports remote clinical decision-making. TUS can be delivered in real time with remote guidance from a sonographic expert. TUS can be used by a variety of health care professionals with minimal ultrasound training. However, as the use of real-time TUS continues to expand to different clinical areas, its clinical effectiveness compared with traditional in-person ultrasound remains unclear. What Did We Do? We received a request related to the use of real-time TUS to support policy decision-making. A literature search was conducted to identify studies examining the clinical effectiveness of real-time TUS compared with conventional in-person ultrasound and any evidence-based guidelines for TUS use in clinical practice. We also report some of the advantages and challenges of TUS as described in the literature. What Did We Find? Real-time TUS was comparable to conventional in-person ultrasound for exam image quality and diagnostic consistency. Exams took, on average, more than 25% (or 6 minutes) longer to complete compared with in-person ultrasound. Real-time TUS was associated with high clinician satisfaction for comfortability, telecommunication quality, exam duration and quality, and accessibility. Several studies reported transient safety-related complications (e.g., increased pressure, pain), patient discomfort or fear, and technical difficulties during 10% of robotic-assisted TUS exams. Real-time TUS was studied in a wide range of clinical indications in various settings, highlighting its growing role and potential for expanded application in clinical practice. No evidence-based guidelines were identified for the use of TUS in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".