Five-year Retrospective Review of Physician and Non-physician Performed Ultrasound in a Canadian Critical Care Helicopter Emergency Medical Service
Bibliographic record
Abstract
OBJECTIVE: To describe the use of prehospital ultrasonography (PHUS) to support interventions, when used by physician and non-physician air medical crew (AMC), in a Canadian helicopter emergency medical service (HEMS). METHODS: A retrospective review was conducted of consecutive patients who underwent ultrasound examination during HEMS care from January 1, 2009 through March 10, 2014. An a priori created data form was used to record patient demographics, type of ultrasound scan performed, ultrasound findings, location of scan, type of interventions supported by PHUS, factors that affected PHUS completion, and quality indicator(s). Data analysis was performed through descriptive statistics, Student's t-test for continuous variables, Z-test for proportions, and Mann-Whitney U Test for nonparametric data. Outcomes included interventions supported by PHUS, factors associated with incomplete scans, and quality indicators associated with PHUS use. Differences between physician and AMC groups were also assessed. RESULTS: PHUS was used in 455 missions, 318 by AMC and 137 by physicians. In combined trauma and medical patients, in the AMC group interventions were supported by PHUS in 26% of cases (95% CI 18-34). For transport physicians the percentage support was found to be significantly greater at 45% of cases (95% CI 34-56) p = < 0.006. Incomplete PHUS scans were common and reasons included patient obesity, lack of time, patient access, and clinical reasons. Quality indicators associated with PHUS were rarely identified. CONCLUSIONS: The use of PHUS by both physicians and non-physicians was found to support interventions in select trauma and medical patients. Key words: emergency medical services; aircraft; helicopter; air ambulance; ultrasonography; emergency care, prehospital; prehospital emergency care.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".