User's perceptions of remote trauma telesonography
Bibliographic record
Abstract
We established a pilot tele-ultrasound system between a rural referring hospital and a tertiary care trauma centre to facilitate telementoring during acute trauma resuscitations. Over a 12-month period, 23 tele-ultrasound examinations were completed. The clinical protocol examined both the Focused Assessment with Sonography for Trauma (FAST) and the Extended FAST (EFAST) for pneumothoraxes. Twenty of the examinations were conducted during acute trauma resuscitations and three during live patient simulations. FAST examinations were completed in all 23 cases and EFAST examinations in 17 cases. There were 18 clinical users, of whom 14 completed a survey (76% response rate). Overall, 93% of respondents were either satisfied or very satisfied with the telemedicine interaction and agreed or strongly agreed that the technology could potentially benefit injured patients in the far north of Canada. In addition, 93% of the respondents felt that the project had improved collegiality between the two institutions involved. The majority of respondents (71%) agreed or strongly agreed that the project had improved their ultrasound skills. We believe that as further experience is obtained, tele-ultrasound will prove to be an important aid to the care of remotely injured and ill patients.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".