Predictive Model for Differentiating Crohn's Disease and Intestinal Tuberculosis: The Story Is Incomplete Without Imaging.

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

Who cites it

3 citing papers in PubMed.

1. A novel multidisciplinary machine learning approach based on clinical, imaging, colonoscopy, and pathology features for distinguishing intestinal tuberculosis from Crohn's disease.Abdominal radiology (New York) · 2024
   Article
2. Differentiating Crohn's disease from intestinal tuberculosis.World journal of gastroenterology · 2019
   Review
3. Gastrointestinal tract involvement in acute pancreatitis: initial findings and follow-up by magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2017
   Article

Corrections and comments

• Comment on
  Predictive Factors for Differentiating Between Crohn's Disease and Intestinal Tuberculosis in Koreans.2016

Authors and funding

5 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

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