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Predicting Local Anesthetic Success in Symptomatic Irreversible Pulpitis: A Machine Learning Study
Sponsor: Jamia Millia Islamia
Summary
This study will develop and internally validate three machine learning models - logistic regression, random forest, and XGBoost - to predict local anesthetic (LA) success in patients undergoing endodontic treatment for symptomatic irreversible pulpitis (SIP). A large retrospective cohort of 4,390 consecutive adult patients treated at a single center (May 2014-October 2025) is being analyzed. The dataset was frozen in October 2025 for this analysis.
Official title: Predicting Local Anesthetic Success in Symptomatic Irreversible Pulpitis: A Comparison of Logistic Regression, Random Forest, and XGBoost With SHAP-Based Interpretability in 4,390 Patients
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
4390
Start Date
2014-05-01
Completion Date
2025-12-16
Last Updated
2026-06-29
Healthy Volunteers
No