Repeat imaging increases detection of delayed pseudoaneurysms in patients with high‐grade solid organ injury following abdominal trauma
Bibliographic record
Abstract
BACKGROUND: Nonoperative management of abdominal trauma can be complicated by the development of delayed pseudoaneurysms. Early intervention reduces the risk of rupture and decreases mortality. The objective of this study is to determine the utility of repeat computed tomography (CT) imaging in detecting delayed pseudoaneurysms in patients with abdominal solid organ injury. METHODS: A retrospective cohort study reviewing Montreal General Hospital registry between 2013 and 2019. Patients with The American Association for the Surgery of Trauma (AAST) grade 3 or higher solid organ injury following abdominal trauma were identified. A chart review was completed, and demographics, mechanism of injury, Injury Severity Score (ISS) score, AAST injury grade, CT imaging reports, and interventions were collected. Descriptive analysis and logistic regression model were completed. RESULTS: We identified 195 patients with 214 solid organ injuries. The average age was 38.6 years; 28.2% were female, 90.3% had blunt trauma, and 9.7% had penetrating trauma. The average ISS score was 25.4 (SD 12.8) in patients without pseudoaneurysms and 19.5 (SD 8.6) in those who subsequently developed pseudoaneurysms. The initial management was nonoperative in 57.0% of the patients; 30.4% had initial angioembolization, and 12.6% went to the operating room. Of the cohort, 11.7% had pseudoaneurysms detected on repeat CT imaging within 72 h. Grade 3 represents the majority of the injuries at 68.0%. The majority of these patients underwent angioembolization. CONCLUSIONS: In patients with high-grade solid organ injury following abdominal trauma, repeat CT imaging within 72 h enabled the detection of delayed development of pseudoaneurysms in 11.7% of injuries. The majority of the patients were asymptomatic.
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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 | 0.008 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".