Application of Tele-Ultrasound in Emergency Medical Services
Bibliographic record
Abstract
In emergency medical services, portable ultrasound scanners have the potential to become new-age stethoscopes for emergency physicians. For trauma cases in particular, portable ultrasound scanners can scan the chest and abdomen of emergency patients both rapidly and conveniently. This study describes the development of tele-ultrasound for pre-diagnosis in a medical emergency setting as a part of the updated Mobile Hospital Emergency Medical System (MHEMS). An emergency medical technician can provide an emergency physician with a patient's ultrasound images and medical information during the patient's pre-hospitalization and transportation period using a combination of the MHEMS, the portable ultrasound scanner, and the onboard 3G communication capabilities. The MHEMS includes a Dispatch and Mission Control Center that facilitates the communication between the Emergency Department of a specified hospital, the systems aboard the ambulance. Early receipt of information relevant to the patient will enhance pre-diagnosis options for on-duty emergency physicians and allow for a hospital's emergency department to promptly prepare necessary surgical instruments or beds. Furthermore, emergency medical technicians can also obtain instructions from on-duty physicians to enhance damage and disaster control ability in critical moments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".