ArticleWorld journal of pediatric surgery2025
Application of artificial intelligence in conjunction with clinical laboratory indicators to aid decision-making for surgical or conservative treatment of pediatric intestinal obstruction.
Article in World journal of pediatric surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Machine learning-based individualized prediction: risk assessment of retinopathy in preterm infants at high altitude.BMC ophthalmology · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Management of pediatric intestinal obstruction remains clinically challenging, particularly regarding the selection between surgical and conservative approaches. This study aimed to develop artificial intelligence (AI) models to support treatment decision-making. Methods: A retrospective analysis was conducted on clinical data from pediatric intestinal obstruction patients. The dataset was split via stratified sampling (70% training/ 30% test), preserving outcome distribution. Predictive models incorporating clinical indicators were developed using machine learning, with evaluation metrics including accuracy, F1-score, Kappa value, positive predictive value (PPV), negative predictive value (NPV), precision-recall curves, calibration plots and decision curve analysis (DCA). Results: Among 765 pediatric patients, 425 responded to conservative treatment while 340 required surgery. The Random Forest model demonstrated optimal performance in the test cohort (area under the curve: 0.953; sensitivity: 0.879; specificity: 0.901; accuracy: 0.892; F1-score: 0.878; Kappa value: 0.780; PPV: 0.878; NPV: 0.905). Calibration, precision-recall, and DCAs indicated favorable clinical applicability. Conclusion: Machine learning integration with clinical indicators shows potential as a decision-support tool for selecting surgical or conservative treatment in pediatric intestinal obstruction.
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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 Socratic graph.