Outcomes of Traumatic Aortic Injury in a Primary Open Surgical Approach Paradigm
Bibliographic record
Abstract
BACKGROUND: Multiple classifications can be used to define the magnitude of aortic injury. The Vancouver Classification (VC) is a new and simplified computed tomography-based Blunt Aortic Injury (BAI) grading system correlating with clinical outcomes. OBJECTIVES: The objectives of this study are: 1) to describe the severity of aortic injury in a center with a predominantly surgical approach to BAI; 2) to correlate the severity of aortic trauma to hospital survival rate and rate of adverse events according to the type of interventions performed during the hospital stay; and 3) to evaluate VC. PATIENTS AND METHODS: All patients referring to the Sacre-Coeur Hospital of Montreal between August 1998 and April 2011 for management of BAI were studied. Two radiologists reviewed all CT scan images individually and classified the aortic injuries using VC. RESULTS: Among the 112 patients presenting with BAI, 39 cases had local CT scans available for reconstruction. Seven patients were identified as suffering from grade I injuries (flap or thrombus of less than 1 cm), 6 from grade II injuries (flap or thrombus of more than 1 cm), and 26 from grade III injuries (pseudoaneurysm). Among the patients with grade I injuries, 57% were treated surgically and 43% medically with a survival rate of 100%. Among the patients with grade II injuries (67% treated surgically and 33% treated medically) survival was also 100%. Among patients with grade III injuries (85% treated surgically, 7% had Thoracic Endovascular Aortic Repair (TEVAR) and 8% treated medically) survival was 95%, 95% and 50%, respectively. There were no significant differences between groups as to clinical outcome. Inter-rater reliability was 0.81. CONCLUSIONS: VC is easy to use and has low inter-observer variability. Low grades of injury were associated with low mortality related to medical treatment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".