Etiology of Small Bowel Thickening on Computed Tomography
Bibliographic record
Abstract
BACKGROUND: Abdominal pain is often evaluated using imaging, most often with computed tomography (CT). While CT is sensitive and specific for certain diagnoses, small bowel thickening is a nonspecific finding on CT with a broad differential diagnosis including infection, inflammation, ischemia and neoplasm. METHOD: A review of medical records of patients who underwent CT scans of the abdomen and pelvis over a one-year period and exhibited small bowel thickening were retrospectively evaluated to determine the final diagnosis. RESULTS: The etiologies of small bowel thickening on CT were as follows: infection (113 of 446 [25.34%]); reactive inflammation (69 of 446 [15.47%]); primary inflammation (62 of 446 [13.90%]); small bowel obstruction (38 of 446 [8.52%]); iatrogenic (33 of 446 [7.40%]); neoplastic (32 of 446 [7.17%]); ascites (30 of 446 [6.73%]); unknown (28 of 446 [6.28%]); ischemic (24 of 446 [5.38%]); and miscellaneous (17 of 446 [3.81%]). CONCLUSION: Infectious and inflammatory (primary or reactive) conditions were the most common cause of small bowel thickening in the present series; these data can be used to formulate a more specific differential diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".