Longer-term rates of survival and reintervention after thoracic endovascular aortic repair (TEVAR) for blunt aortic injury: a retrospective population-based cohort study from Ontario, Canada
Bibliographic record
Abstract
Objectives: Blunt aortic injury (BAI) is associated with a high rate of mortality. Thoracic endovascular aortic repair (TEVAR) has emerged as the preferred treatment option for patients with BAI. In this study, we compare the longer-term outcomes of patients receiving TEVAR with other treatment options for BAI. Methods: We conducted a retrospective cohort study using administrative health data on patients with BAI in Ontario, Canada between 2009 and 2020. Patients with BAI and who survived at least 24 hours after hospital admission were identified using diagnostic codes. We classified patients as having received TEVAR, open surgical, hybrid repair, or medical management as their initial treatment approach based on procedure codes. The primary outcome was survival to maximum follow-up. Secondary outcomes included aorta-related mortality or aortic reintervention. Cox's proportional hazards models were used to estimate the effect of TEVAR on survival. Results: 427 patients with BAI were followed for a median of 3 years (IQR: 1-6 years), with 348 patients (81.5%) surviving. Survival to maximum follow-up did not differ between treatment groups: TEVAR: 79%, surgical repair: 63.6%, hybrid repair: 85.7%, medical management: 83.3% (p=0.10). In adjusted analyses, TEVAR was not associated with improved survival compared with surgical repair (HR: 0.6, 95% CI: 0.3 to 1.6), hybrid repair (HR: 1.4, 95% CI: 0.5 to 3.6), or medical management (HR: 1.5, 95% CI: 0.8 to 2.6). Aortic reinterventions were required in only 2.6% of surviving patients but were significantly more common in the TEVAR group (p<0.01). Conclusions: The longer-term survival from BAI appears highly favorable with low rates of reintervention and death in the years after injury, regardless of the initial treatment approach. Level of evidence: IV, Therapeutic study.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".