The feasibility of nurse practitioner-performed, telementored lung telesonography with remote physician guidance - ‘a remote virtual mentor’
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Point-of-care ultrasound (POC-US) use is increasingly common as equipment costs decrease and availability increases. Despite the utility of POC-US in trained hands, there are many situations wherein patients could benefit from the added safety of POC-US guidance, yet trained users are unavailable. We therefore hypothesized that currently available and economic 'off-the-shelf' technologies could facilitate remote mentoring of a nurse practitioner (NP) to assess for recurrent pneumothoraces (PTXs) after chest tube removal. METHODS: The simple remote telementored ultrasound system consisted of a handheld ultrasound machine, head-mounted video camera, microphone, and software on a laptop computer. The video output of the handheld ultrasound machine and a macroscopic view of the NP's hands were displayed to a remote trauma surgeon mentor. The mentor instructed the NP on probe position and US machine settings and provided real-time guidance and image interpretation via encrypted video conferencing software using an Internet service provider. Thirteen pleural exams after chest tube removal were conducted. RESULTS: Thirteen patients (26 lung fields) were examined. The remote exam was possible in all cases with good connectivity including one trans-Atlantic interpretation. Compared to the subsequent upright chest radiograph, there were 4 true-positive remotely diagnosed PTXs, 2 false-negative diagnoses, and 20 true-negative diagnoses for 66% sensitivity, 100% specificity, and 92% accuracy for remotely guided chest examination. CONCLUSIONS: Remotely guiding a NP to perform thoracic ultrasound examinations after tube thoracostomy removal can be simply and effectively performed over encrypted commercial software using low-cost hardware. As informatics constantly improves, mentored remote examinations may further empower clinical care providers in austere settings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it