Just-in-Time Cost-Effective Off-the-Shelf Remote Telementoring of Paramedical Personnel in Bedside Lung Sonography—A Technical Case Study
Bibliographic record
Abstract
PURPOSE: Remote telementored ultrasound (RTMUS) is a new discipline that allows a remote expert to guide variably experienced clinical responders through focused ultrasound examinations. We used the examination of the pleural spaces after tube thoracostomy (TT) removal by a nurse with no prior ultrasound experience as an illustrative but highly accurate example of the technique using a simple cost-effective system. MATERIALS AND METHODS: The image outputs of a handheld ultrasound machine and a head-mounted Web camera were input into a customized graphical user interface and streamed over a freely available voice over Internet protocol system that allowed two-way audio and visual communication between the novice examiner and the remote expert. The bedside nurse was then guided to examine the anterior chest of a patient who had recently had bilateral TTs removed. The team sought to determine the presence or absence of any recurrent pneumothoraces using the standard criteria for the ultrasound diagnosis of post-removal pneumothorax (PTXs). An upright chest radiograph (CXR) was obtained immediately after the RTMUS examination. RESULTS: The RTMUS system enabled the novice user to learn how to hold the ultrasound probe, where to place it on the chest, and thereafter to diagnose a subtle unilateral PTX characterized as "tiny" on the subsequent formal CXR report. CONCLUSIONS: As ultrasound has almost limitless clinical utility, using simple but advanced informatics and communication technologies has potential to improve worldwide healthcare delivery. RTMUS could be used both to enhance the information content as well as to digitally document important physiologic findings in any clinical encounter wherever a portable ultrasound and Internet connectivity are available.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".