A nomogram for predicting the risk of temporomandibular disorders in university students.

ABSTRACT:

Objectives

Temporomandibular disorders (TMDs) have a relatively high prevalence among university students. This study aimed to identify independent risk factors for TMD in university students and develop an effective risk prediction model.

Methods

This study included 1,122 university students from four universities in Changchun City, Jilin Province, as subjects. Predictive factors were screened by using the least absolute shrinkage and selection operator (LASSO) regression and the machine learning Boruta algorithm in the training cohort. A multifactorial logistic regression analysis was used to construct a TMD risk prediction model. Internal validation of the model was conducted via bootstrap resampling, and an external validation cohort comprised 205 university students und

SUBMITTER: Cui Y 

PROVIDER: S-EPMC11380197 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature