ArticleGastroenterology report2026
Development and validation of a Bayesian network-based surgical risk-prediction tool for patients with small-bowel stricturing Crohn's disease.
Article in Gastroenterology report, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patients with small-bowel stricturing Crohn's disease (sbsCD) usually have a higher risk of intestinal surgical resection. We aimed to develop a machine-learning model for predicting the 1-year surgery risk in these patients. Methods: This study included 520 retrospectively enrolled patients with sbsCD (training cohort, Results: There were 158 (24.4%) Crohn's disease (CD)-related surgeries during the 1-year follow-up. Eight selected predictors of surgery included penetrating lesions, nonuse of biologics, nonuse of corticosteroids, a CD obstructive score of ≥3, endoscopic strictures, anemia, radiologic luminal narrowing, and prestenotic dilation. Among the six models evaluated, the Tree-Augmented Naïve Bayes (TAN) model demonstrated optimal performance, with a mean AUC of 0.878. A further prospective validation cohort verified the efficacy of the model, with 87.5% specificity, 76.7% sensitivity, and 84.9% accuracy for predicting 1-year surgery. Four simplified BN-based risk matrices were constructed for practical use. An online prediction tool is available at http://prebn.site/. Conclusion: We developed and validated a TAN-based BN model incorporating clinical and radiological features to accurately predict the 1-year surgical risk for clinical application in patients with sbsCD, thereby providing a promising tool for decision-making.
Indexed as
Identifiers
What 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.