ArticleThe American journal of gastroenterology2016
Predictive Factors for Differentiating Between Crohn's Disease and Intestinal Tuberculosis in Koreans.
Article in The American journal of gastroenterology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses that pooled it.
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
32 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Frequency, outcomes, and need for intervention in stricturing gastrointestinal tuberculosis: a systematic review and meta-analysis.BMC gastroenterology · 2023Pooled it
- Meta-Analytic Bayesian Model For Differentiating Intestinal Tuberculosis from Crohn's Disease.The American journal of gastroenterology · 2017Pooled it
- Gastrointestinal tuberculosis: A systematic review of epidemiology, presentation, diagnosis and treatment.Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology AssociationPooled it
- The Overlap Between Crohn's Disease and Intestinal Tuberculosis: A Never-Ending Story.Medicina (Kaunas, Lithuania) · 2026Review
- Identification of Potential Functional Modules and Diagnostic Genes for Crohn's Disease Based on Weighted Gene Co-expression Network Analysis and LASSO Algorithm.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2025Article
- Artificial intelligence in inflammatory bowel disease endoscopy - a review of current evidence and a critical perspective on future challenges.Therapeutic advances in gastroenterology · 2025Review
- Article
- 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) · 2024Article
- Deep learning model to differentiate Crohn's disease from intestinal tuberculosis using histopathological whole slide images from intestinal specimens.Virchows Archiv : an international journal of pathology · 2024Article
- Article
- Intestinal tuberculosis with small bowel stricture and hemorrhage as the predominant manifestation: Three case reports.World journal of gastrointestinal surgery · 2024Article
- Differentiating gastrointestinal tuberculosis and Crohn's disease- a comprehensive review.BMC gastroenterology · 2023Review
- Computed tomography enterography-based multiregional radiomics model for differential diagnosis of Crohn's disease from intestinal tuberculosis.Abdominal radiology (New York) · 2023Article
- Article
- Differential diagnosis of Crohn's disease and intestinal tuberculosis: development and assessment of a nomogram prediction model.BMC gastroenterology · 2022Article
- Applying logistic LASSO regression for the diagnosis of atypical Crohn's disease.Scientific reports · 2022Article
- Enterogenous Microbiotic Markers in the Differential Diagnosis of Crohn's Disease and Intestinal Tuberculosis.Frontiers in immunology · 2022Article
- Development and validation of a new algorithm model for differential diagnosis between Crohn's disease and intestinal tuberculosis: a combination of laboratory, imaging and endoscopic characteristics.BMC gastroenterology · 2021Article
- Application value of tissue tuberculosis antigen combined with Xpert MTB/RIF detection in differential diagnoses of intestinal tuberculosis and Crohn's disease.BMC infectious diseases · 2021Article
- Performance of Diagnostic Model for Differentiating Between COVID-19 and Influenza: A 2-Center Retrospective Study.Medical science monitor : international medical journal of experimental and clinical research · 2021Article
Corrections and comments
- Commented on by
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesA differential diagnosis between intestinal tuberculosis (ITB) and Crohn's disease (CD) is challenging. The aim of this study was to investigate the clinical, endoscopic, and histological features and to create a predictive score model for differentiating CD and ITB.
methodsIn total, 261 patients, 99 with ITB and 162 with CD, were recruited from seven tertiary centers from 2005 to 2013 and reviewed retrospectively. For the creation of a validated model, parameters were selected by univariate logistic regression and receiver operating characteristic curve analyses. Then, the prediction model was established on the basis of β-coefficients of the multivariate logistic regression. For the validation of the model, the same regression equation was tested on the other group.
resultsAge, diarrhea, ring-shaped ulcer, longitudinal ulcer, sigmoid involvement, suspicious radiological pulmonary tuberculosis, and gender were selected as the factors for a seven-marker model. In the seven-marker model of the validation data set, the sensitivity, specificity, positive predictive value, and negative predictive value with a cutoff level of 0.35 were 98.0, 92.4, 88.9, and 98.6, respectively.
conclusionsThe seven-marker model seems to be highly reliable for differentiating between ITB and CD and could be conveniently used by clinicians to obtain results.
Indexed as
Identifiers
27296940What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.