Help Is in Your Pocket: The Potential Accuracy of Smartphone- and Laptop-Based Remotely Guided Resuscitative Telesonography
Bibliographic record
Abstract
BACKGROUND: Ultrasound (US) examination has many uses in resuscitation, but to use it to its full effectiveness typically requires a trained and proficient user. We sought to use information technology advances to remotely guide US-naive examiners (UNEs) using a portable battery-powered tele-US system mentored using either a smartphone or laptop computer. MATERIALS AND METHODS: A cohort of UNEs (5 tactical emergency medicine technicians, 10 ski-patrollers, and 4 nurses) was guided to perform partial or complete Extended Focused Assessment with Sonography of Trauma (EFAST) examinations on both a healthy volunteer and on a US phantom, while being mentored by a remote examiner who viewed the US images over either an iPhone(®) (Apple, Cupertino, CA) or a laptop computer with an inlaid depiction of the US probe and the "patient," derived from a videocamera mounted on the UNE's head. Examinations were recorded as still images and over-read from a Web site by seven expert reviewers (ERs) (three surgeons, two emergentologists, and two radiologists). Examination goals were to identify lung sliding (LS) documented by color power Doppler (CPD) in the human and to identify intraperitoneal (IP) fluid in the phantom. RESULTS: All UNEs were successfully mentored to easily and clearly identify both LS (19 determinations) and IP fluid (14 determinations), as assessed in real time by the remote mentor. ERs confirmed IP fluid in 95 of 98 determinations (97%), with 100% of ERs perceiving clinical utility for the abdominal Focused Assessment with Sonography of Trauma. Based on single still CPD images, 70% of ERs agreed on the presence or absence of LS. In 16 out of 19 cases, over 70% of the ERs felt the EFAST exam was clinically useful. CONCLUSIONS: UNEs can confidently be guided to obtain critical findings using simple information technology resources, based on the receiving/transmitting device found in most trauma surgeons' pocket or briefcase. Global US mentoring requires only Internet connectivity and initiative.
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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 | 0.018 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".