Potential Use of Remote Telesonography as a Transformational Technology in Underresourced and/or Remote Settings
Bibliographic record
Abstract
Mortality and morbidity from traumatic injury are twofold higher in rural compared to urban areas. Furthermore, the greater the distance a patient resides from an organized trauma system, the greater the likelihood of an adverse outcome. Delay in timely diagnosis and treatment contributes to this penalty, regardless of whether the inherent barriers are geographic, cultural, or socioeconomic. Since ultrasound is noninvasive, cost-effective, and portable, it is becoming increasingly useful for remote/underresourced (R/UR) settings to avoid lengthy patient travel to relatively inaccessible medical centers. Ultrasonography is a user-dependent, technical skill, and many, if not most, front-line care providers will not have this advanced training. This is particularly true if care is being provided by out-of-hospital, "nontraditional" providers. The human exploration of space has forced the utilization of information technology (IT) to allow remote experts to guide distant untrained care providers in point-of-care ultrasound to diagnose and manage both acute and chronic illness or injuries. This paradigm potentially brings advanced diagnostic imaging to any medical interaction in a setting with internet connectivity. This paper summarizes the current literature surrounding the development of teleultrasound as a transformational technology and its application to underresourced settings.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".