CT diagnosis and destiny of acute aortic intramural hematoma
Bibliographic record
Abstract
Acute aortic intramural hematoma (IMH) is a relatively uncommon but potentially life-threatening aortic disease that can occur primarily in hypertensive and atherosclerotic patients. The course of IMH varies widely, with the condition either regressing, remaining stable, or progressing until it leads to outward rupture or intimal layer disruption, eventually resulting in overt aortic dissection. Therefore, poor prognostic computed tomography (CT) features must be promptly recognized and reported by the radiologist. In emergency departments, readily accessible non-invasive CT angiography is crucial for achieving a rapid and accurate diagnosis essential for appropriate management. For Type A and B aortic dissection, surgery is typically recommended in Western countries for patients with Stanford Type A IMH and those experiencing irrepressible pain. For Stanford Type B IMH patients without complications or incessant pain, medical treatment is suggested but with imaging follow-up. In complicated Stanford Type B situations, thoracic endovascular aortic repair (TEVAR) is currently indicated. This review aims to present pathophysiology, CT diagnosis, and IMH fate and provide the reader CT image-based review of the CT diagnostic criteria, complications, and associated critical prognostic findings of this rather rare aortic disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".