Simple, Almost Anywhere, With Almost Anyone: Remote Low-Cost Telementored Resuscitative Lung Ultrasound
Bibliographic record
Abstract
BACKGROUND: Apnea (APN) and pneumothorax (PTX) are common immediately life-threatening conditions. Ultrasound is a portable tool that captures anatomy and physiology as digital information allowing it to be readily transferred by electronic means. Both APN and PTX are simply ruled out by visualizing respiratory motion at the visceral-parietal pleural interface known as lung sliding (LS), corroborated by either the M-mode or color-power Doppler depiction of LS. We thus assessed how economically and practically this information could be obtained remotely over a cellular network. METHODS: Ultrasound images were obtained on handheld ultrasound machines streamed to a standard free internet service (Skype) using an iPhone. Remote expert sonographers directed remote providers (with variable to no ultrasound experience) to obtain images by viewing the transmitted ultrasound signal and by viewing the remote examiner over a head-mounted webcam. Examinations were conducted between a series of remote sites and a base station. Remote sites included two remote on-mountain sites, a small airplane in flight, and a Calgary household, with base sites located in Pisa, Rome, Philadelphia, and Calgary. RESULTS: In all lung fields (20/20) on all occasions, LS could easily and quickly be seen. LS was easily corroborated and documented through capture of color-power Doppler and M-mode images. Other ultrasound applications such as the Focused Assessment with Sonography for Trauma examination, vascular anatomy, and a fetal wellness assessment were also demonstrated. CONCLUSION: The emergent exclusion of APN-PTX can be immediately accomplished by a remote expert economically linked to almost any responder over cellular networks. Further work should explore the range of other physiologic functions and anatomy that could be so remotely assessed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".