Background: Older adults who have undergone surgery for oral tumors are at increased risk of impaired masticatory rhythm. This study investigated the correlations between masticatory rhythm, objective masticatory performance, and subjective masticatory performance as well as factors related to masticatory rhythm. Methods: The participants were 44 adults (24 men, 20 women; age range 42~90 years old) who had undergone maxillectomy, mandibulectomy, or glossectomy and were rehabilitated with a maxillofacial prosthesis. The number of functional contact teeth pairs was confirmed by intraoral examination. Chewing rate, cycle duration, coefficient of variation (CV) for cycle duration (reflecting the stability of masticatory rhythm), and mixing ability were measured simultaneously using a mastication movement rhythm tracking device during gum chewing. Maximum occlusal force was measured using the dental prescale system. Patients’ perception of chewing ability was rated using a questionnaire. Results: The Spearman’s rank correlation test revealed that mixing ability, patient-rated masticatory scores, cycle duration, CV for cycle duration, and maximum occlusal force showed significant correlations with chewing rate. Multiple linear regression analysis identified mixing ability and the CV for cycle duration as significant predictors of masticatory rhythm. Conclusions: Factors associated with a faster chewing rate were higher mixing ability and masticatory scores, greater maximum occlusal force, shorter cycle duration, and smaller CV for cycle duration. Stable masticatory rhythm and mixing ability are significant predictors of chewing rate. Poor masticatory performance and unstable masticatory rhythm can result in slower chewing and thus a higher risk of inadequate dietary intake.

Background: This investigation sought to cross validate the predictors of tongue pressure recovery in elderly patients’ post-treatment for head and neck tumors, leveraging advanced machine learning techniques. Methods: By employing logistic regression, support vector regression, random forest, and extreme gradient boosting, the study analyzed an array of variables including patient demographics, surgery types, dental health status, and age, drawn from comprehensive medical records and direct tongue pressure assessments. Results: Among the models, logistic regression emerged as the most effective, demonstrating an accuracy of 0.630 [95% confidence interval (CI): 0.370–0.778], F1 score of 0.688 [95% confidence interval (CI): 0.435–0.853], precision of 0.611 [95% confidence interval (CI): 0.313–0.801], recall of 0.786 [95% confidence interval (CI): 0.413–0.938] and an area under the receiver operating characteristic curve of 0.626 [95% confidence interval (CI): 0.409–0.806]. This model distinctly highlighted the significance of glossectomy (p = 0.039), the presence of functional teeth (p = 0.043), and the patient’s age (p = 0.044) as pivotal factors influencing tongue pressure, setting the threshold for statistical significance at p < 0.05. Conclusions: The analysis underscored the critical role of glossectomy, the presence of functional natural teeth, and age as determinants of tongue pressure in logistics regression, with the presence of natural teeth and the tumor site located in the tongue consistently emerging as the key predictors across all computational models employed in this study.