Evolution of the Incidence, Management, and Mortality of Blunt Thoracic Aortic Injury: A Population-Based Analysis
Bibliographic record
Abstract
BACKGROUND: In the last decade, CT angiography has become the dominant diagnostic modality for blunt aortic injury and endovascular repair has become the leading aortic repair strategy. The impact of these shifts on incidence, aortic repair rate, and mortality remains poorly characterized. Our objective was to perform a population-based analysis of secular trends in the incidence, management, and in-hospital mortality of blunt thoracic aortic injury. STUDY DESIGN: From the population-based Canadian National Trauma Registry, we identified a cohort of all adults hospitalized between April 2002 and March 2010 with a diagnosis of thoracic aortic injury after blunt trauma. Trends over time in the incidence of hospitalization, frequency and type of aortic repair, as well as risk-adjusted in-hospital mortality, were evaluated. RESULTS: A total of 487 incident cases of blunt thoracic aortic injury were identified. During the study period, the incidence of hospitalization for blunt thoracic aortic injury remained stable (trend p = 0.16). Although the proportion of repairs undertaken via an endovascular approach increased (11% to 78% of repairs; trend p < 0.001), the frequency of any repair (endovascular or open) declined (55% to 36%; trend p = 0.003). Across all patients, when controlling for age, sex, mechanism of injury, and presence of severe extrathoracic injuries, mortality remained unchanged during the study period (odds ratio = 0.92 per 1 year; 95% CI, 0.82-1.03). However, in patients managed nonoperatively, risk-adjusted mortality decreased over time (odds ratio = 0.85 per 1 year; 95% CI, 0.80-0.98). CONCLUSIONS: The increasing frequency of patients managed nonoperatively and decreasing risk-adjusted mortality in these patients suggests that defining the evolving role of nonoperative management should be a major focus of research in the endovascular era.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".