Computed Tomography Angiography Assessment of Acute Aortic Syndromes: Classification, Differentiating Imaging Features, and Imaging Interpretation Pitfalls
Bibliographic record
Abstract
An acute aortic syndrome (AAS) is an important life-threatening condition that requires early detection and management. Acute intramural hematoma (IMH), aortic dissection (AD) and penetrating atherosclerotic ulcer (PAU) are included in AAS. ADs can be classified using the well-known Stanford or DeBakey classification systems. However, these classification systems omit description of arch dissections, anatomic variants, and morphologic features that impact outcome. The Society for Vascular Surgery and Society of Thoracic Surgeons (SVS-STS) have recently introduced a classification system that classifies ADs according to the location of the entry tear (primary intimomedial tear, PIT) and the proximal and distal extent of involvement, but does not include description of all morphologic features that may have diagnostic and prognostic significance. This review describes these classification systems for ADs and other AAS entities as well as their limitations. Typical computed tomography angiography (CTA) imaging appearance and differentiating features of ADs, limited intimal tears (LITs), IMHs, intramural blood pools (IBPs), ulcer-like projections (ULPs), and PAUs will be discussed. Furthermore, this review highlights common imaging interpretation pitfalls, what should be included in a comprehensive CTA report, and provides a brief overview of current management options.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".