Multidetector computed tomography of mesenteric ischaemia
Bibliographic record
Abstract
UNLABELLED: Mesenteric ischaemia comprises a broad, heterogeneous group of diseases characterised by inadequate blood supply to the small or large bowel. Acute mesenteric ischaemia is a surgical emergency, with significant associated morbidity and mortality. Because the clinical presentation of mesenteric ischaemia is variable and often nonspecific, a high index of clinical and radiologic suspicion is required for early diagnosis. The severity of mesenteric ischaemia ranges from transient, localised ischaemia to frank necrosis of the bowel. The most common causes of acute mesenteric ischaemia are embolic and thrombotic occlusion of the superior mesenteric artery, whereas chronic mesenteric ischaemia is almost always associated with generalised atherosclerotic disease. Multidetector computed tomography (MDCT) angiography is the preferred imaging test for acute and chronic mesenteric ischaemia. MDCT is useful in making a prompt, more precise diagnosis of mesenteric ischaemia, as well as identifying the cause and potential complications, which are key to reducing patient morbidity and mortality. In this article, we review the clinical features and aetiologies of mesenteric ischaemia and illustrate the imaging manifestations on MDCT. MAIN MESSAGES: • Acute and chronic mesenteric ischaemia are morbid conditions challenging to diagnose. • MDCT is the first-line imaging test for evaluating patients with suspected mesenteric ischaemia. • Bowel findings include wall thickening, abnormal enhancement, pneumatosis and luminal dilation. • Vascular occlusion, portomesenteric venous gas, mesenteric congestion and free air can be seen.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".