Rupture signs on computed tomography, treatment, and outcome of abdominal aortic aneurysms
Bibliographic record
Abstract
OBJECTIVES: Abdominal aortic aneurysm (AAA) rupture has a high mortality rate. Although the diagnosis of a ruptured AAA is usually straightforward, detection of impending rupture signs can be more challenging. Early diagnosis of impending AAA rupture can be lifesaving. Furthermore, differentiating between impending and complete rupture has important repercussions on patient management and prognosis. The purpose of this article is to classify and illustrate the entire spectrum of AAA rupture signs and to review current treatment options for ruptured AAAs. METHODS: Using medical illustrations supplemented with computed tomography (CT), this essay showcases the various signs of impending rupture and ruptured AAAs. Endovascular aneurysm repair (EVAR) and open surgical repair are also discussed as treatment options for ruptured AAAs. RESULTS: CT imaging findings of ruptured AAAs can be categorised according to location: intramural, luminal, and extraluminal. Intramural signs generally indicate impending AAA rupture, whereas luminal and extraluminal signs imply complete rupture. EVAR has emerged as an alternative and possibly less morbid method to treat ruptured AAAs. CONCLUSIONS: AAA rupture occurs at the end of a continuum of growth and wall weakening. This review describes the CT imaging findings that may help identify impending rupture prior to complete rupture. TEACHING POINTS: • AAA rupture occurs at the end of a continuum of growth and wall weakening. • Intramural imaging findings indicate impending AAA rupture. • Luminal and extraluminal imaging findings imply complete AAA rupture. • Some imaging findings are not specific to AAA ruptures and can be seen in other pathologies. • EVAR has emerged as an alternative and possibly less morbid method of treating ruptured AAAs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".