The Ongoing Challenge of Acute Mesenteric Ischemia
Bibliographic record
Abstract
BACKGROUND: The lethality of acute mesenteric ischemia (AMI) remains quite high with 50-70%. The main reasons for that are the fact that AMI is rarely taken into consideration by the differential diagnosis of acute abdomen, the time-consuming diagnostic process, and the lack of a standardized therapeutic concept. The present interdisciplinary review aims to increase awareness among physicians and to help improve clinical outcomes. METHODS: This clinical therapeutic review is based on author expertise as well as a selective literature survey in PubMed based on the term 'mesenteric ischemia', combined with the terms 'arterial', 'clinical presentation', 'diagnosis', 'therapy', 'surgery', and 'interventional radiology'. Based on these search results as well as on the guidelines of the German Society of Vascular Surgery, the American College of Cardiology, and the American Heart Association, we present an interdisciplinary treatment concept. RESULTS: AMI is a vascular emergency that can be successfully treated only within the first hours after the onset of symptoms. Computed tomography angiography is the diagnostic method of choice. Intensive care unit treatment can prevent the occurrence of multiple organ failure. Treatment primarily consists of the revascularization of the mesenteric arteries. Endovascular techniques should be given priority, whereas signs of peritonitis or a central arterial occlusion with high thrombus load primarily require a surgical approach in order to save time and increase patient safety. Additional bowel resections can play a significant role in the treatment of intestinal sepsis. CONCLUSION: Prompt and goal-oriented diagnosis and consistent treatment of AMI within 4-6 h from the onset of symptoms can be decisive for the reduction of AMI-associated lethality. In order for this to happen, a standardized concept of emergency treatment needs to be implemented.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".