A pictorial review of acute aortic syndrome: discriminating and overlapping features as revealed by ECG-gated multidetector-row CT angiography
Bibliographic record
Abstract
BACKGROUND: The term "acute aortic syndrome" (AAS) encompasses a spectrum of life-threatening conditions characterized by acute aortic pain. AAS traditionally embraces three abnormalities including classic aortic dissection, intramural haematoma, and penetrating atherosclerotic ulcer. Although the underlying etiologies and conditions predisposing to AAS are diverse, the clinical features are indistinguishable. METHODS: Multidetector-row computed tomography (CT) with electrocardiographic gating (ECG-gated MDCT) has greatly improved imaging of acute thoracic aortic diseases by virtually eliminating pulsation artifacts transmitted from cardiac motion and reveals subtle aortic abnormalities, which have been difficult to recognize by conventional non-gated CT. RESULTS: While these advances in imaging technology provide additional discriminating features of acute aortic diseases, they also reveal a range of overlapping features of these life-threatening conditions that not uncommonly are dynamic and evolving. These overlapping and transitional features may be a major source of misunderstanding, confusion, and controversy for diseases that cause AAS. CONCLUSION: In this pictorial review, we describe the discriminating and typical imaging features as revealed by modern ECG-gated MDCT angiography. In addition to the discriminating features, recognition of the overlapping and transitional features in AAS will allow a more comprehensive understanding of their underlying pathophysiologic conditions and their natural history, and may improve therapeutic management. MAIN MESSAGES: • The superior visualization of ECG-gated CTA improves the diagnostic accuracy of acute aortic syndrome. • ECG-gated CTA provides discriminating features of underlying pathophysiologic conditions of AAS. • Also, recognition of the overlapping features in AAS will allow a more comprehensive understanding.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".