Computed Tomography in Emergency Diagnosis and Management Considerations of Small Bowel Obstruction for Surgical vs. Non-surgical Approach
Bibliographic record
Abstract
BACKGROUND: Background: Small Bowel Obstruction (SBO) accounts for 15% of abdominal pain complaints referred to emergency departments and imposes significant financial burdens on the healthcare system. The most common symptom and sign of SBO is the absence of stool or flatus passsage and abdominal distension, respectively. Patients who do not demonstrate severe clinical or imaging findings are typically treated with conservative approaches. Patients with clinical signs of sepsis or physical findings of peritonitis are often instantly transferred to the operating room without supplementary imaging assessment. However, in cases where symptoms are non-specific or physical examination is challenging, such as in cases with loss of consciousness, the diagnosis can be complicated. This paper discusses the key findings identifiable on Computed Tomography (CT) which are vital for the emergent triage, proper treatment and appropriate decision making in patients with suspected SBO. METHODS: Narrative review of the literature. RESULTS: CT plays a key role in emergent triage, proper treatment and decision making and provides high sensitivity, specificity, and accuracy in the detection of early-stage obstruction and acute intestinal vascular compromise. CT can also differentiate between various etiologies of SBO entity which is considered an important criterion in the triage of patients into surgical vs. non-surgical treatment. CONCLUSION: There Key CT findings which may suggest a need for surgical treatment include mesenteric edema, lack of the small-bowel feces, bowel wall thickening, fat stranding in the mesentery, and intraperitoneal fluid which are predictive of urgent surgical exploration.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".