Computed Tomography Angiography Utilization in Lower Extremity Trauma: Insights From a Canadian Level I Trauma Centre
Bibliographic record
Abstract
Introduction: Computed tomography angiography (CTA) plays an important role in assessing patients with suspected lower extremity traumatic vascular injury. However, CTA overutilization has been reported in some centres, and improper use has been linked to increased healthcare costs and prolonged Emergency Department wait times. This study evaluated CTA utilization in a Canadian Level I trauma centre, determined the rate of positive CTA studies requiring intervention, and identified factors that may reduce unnecessary examinations. Methods and Materials: This retrospective study included trauma patients who underwent lower extremity CTA between January 2020 and September 2024. Data regarding patient demographics, mechanism of injury, physical exam and computed tomography findings, ankle-brachial index value, and interventions were collected and evaluated. Statistical analysis included descriptive statistics and chi-square or Fisher’s exact tests for categorical associations. Results: Six hundred twelve patients (82% male, median age 32 years) were included. Forty-six percent had a normal physical exam, and CTA was positive in 27% of cases. Eight percent of patients required an intervention, all of whom had at least one hard sign of vascular injury. A statistically significant association was identified between hard signs of a vascular injury and positive CTA findings ( P < .001) and major vascular injuries ( P < .01). No patients with a normal physical exam and a positive CTA required intervention. Conclusion: Nearly half of the CTA studies were performed on patients with a normal physical exam, none requiring intervention. Our findings suggest that implementing institution-specific appropriate criteria may reduce unnecessary CTA studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".