Role of colonoscopic biopsy in distinguishing between Crohn’s disease and intestinal tuberculosis
Bibliographic record
Abstract
BACKGROUND: The histological differential diagnosis of Crohn's disease and intestinal tuberculosis can be very challenging, as both are chronic granulomatous disorders with overlapping histological features. AIM: To evaluate selected clinical and histological parameters in colonic biopsy specimens for their ability to discriminate between Crohn's disease and intestinal tuberculosis. METHODS: 25 patients with Crohn's disease and 18 patients with intestinal tuberculosis were selected for this study on the basis of established clinical, radiological and histological criteria. Clinical data and selected histological parameters in colonoscopic biopsy specimens were assessed retrospectively. A total of 103 and 41 biopsy sites were evaluated in patients with Crohn's disease and intestinal tuberculosis, respectively. RESULTS: Clinical parameters helpful in differentiating intestinal tuberculosis from Crohn's disease included chest radiographic features of tuberculosis (56% v 0%), perianal fistulae (0% v 40%) and extraintestinal manifestations of Crohn's disease (0% v 40%). Histopathological features that seemed to reliably differentiate between intestinal tuberculosis and Crohn's disease included confluent granulomas, > or =10 granulomas per biopsy site and caseous necrosis (in biopsy samples of 50%, 33% and 22% of patients with intestinal tuberculosis, respectively, v 0% of patients with Crohn's disease). Features that were observed more often in patients with intestinal tuberculosis than in those with Crohn's disease included granulomas exceeding 0.05 mm(2) (67% v 8%), ulcers lined by conglomerate epithelioid histiocytes (61% v 8%) and disproportionate submucosal inflammation (67% v 10%). CONCLUSION: Clinical features and selected histological parameters in colonoscopic biopsy specimens can help in differentiating between Crohn's disease and intestinal tuberculosis.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".