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COMPLETED
NCT07672678

Predicting Local Anesthetic Success in Symptomatic Irreversible Pulpitis: A Machine Learning Study

Sponsor: Jamia Millia Islamia

View on ClinicalTrials.gov

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