Observational studyClinical and translational gastroenterology2025
Integrating Etiological Insights With Machine Learning for Precision Diagnosis of Obstructive Jaundice: Findings From a High-Volume Center.
Observational study in Clinical and translational gastroenterology, 2025. 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
12 authors.
Funding
Abstract
introductionLarge-scale cohort studies exploring the etiology of obstructive jaundice (OJ) are scarce, with current serum-based diagnostic markers offering suboptimal performance. This study leverages the largest retrospective cohort of patients with OJ to date to investigate its disease spectrum and to develop a novel diagnostic system.
methodsThis study involves 2 retrospective observational cohorts. The biliary surgery cohort (BS cohort, n = 349) served for initial data exploration and external validation of machine learning (ML) models. The large general cohort (LG cohort, n = 5,726) enabled an in-depth analysis of etiologies and the determination of relevant diagnostic indicators, in addition to supporting ML model development. Interpretable ML techniques were used to derive insights from the models.
resultsThe LG cohort highlighted a diverse disease spectrum of OJ, including cholangiocarcinoma (10.39% distal, 10.01% perihilar, and 5.59% intrahepatic), pancreatic adenocarcinoma (19.11%), and common bile duct stones (18.27%) as leading causes. Traditional serum markers such as carbohydrate antigen 19-9 and carcinoembryonic antigen lacked stand-alone diagnostic accuracy. Two ML-based models (collectively termed the ML of OJ based on common laboratory tests model) were developed: a classifier to differentiate benign from malignant causes (AUROC = 0.862) and a multiclass model to further stratify malignant and benign diseases (ACC = 0.777). Interpretable ML tools provided clarity on critical features, offering actionable insights and enhancing transparency in the decision-making process. DISCUSSION: This study elucidates the etiological spectrum of OJ, meanwhile providing a practical and interpretable ML-based diagnostic tool. By leveraging large-scale clinical data, our model provides a rapid and reliable primary assessment for patients with OJ, enabling clinicians to identify potential etiologies and guide further diagnostic workup.
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.