ClinicalTrials.gov
public full text
Public Domain
Application of Machine Learning Models to Reduce Need for Diagnostic EUS or MRCP in Patients With Intermediate Likelihood of Choledocholithiasis
Machine learning predictive model can help in stratifying heterogenous intermediate likelihood group to reduce need for EUS or MRCP in selected subgroup of patients.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Hepatic Steatosis Severity Prediction in Nonobese Individuals: Machine Learning Model Development and Validation.#298844
- Clinical Trial Comparing ERCP vs ERCP and Transmural Gall Bladder Drainage#848655
- GALLVIA Trial: EUS-Guided vs Transpapillary Gallbladder Drainage for Acute Calculous Cholecystitis#1172733
- Acute Cholecystitis With Concomitant Choledocholithiasis: Unicentric Study of Prevalence and Predictive Factors#982735
- Dutch-TIMELINESS: optimising choledocholithiasis treatment in the Netherlands - protocol of a national implementation project.#409391
Record · ID 884570
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive