Abstract

Background/Objectives: A multivariable prediction model was developed and externally validated to estimate an individual’s current need to visit a dentist among young to middle-aged adults. The objective was to provide an accessible, non-invasive screening tool for independent home self-assessment or integration within routine healthcare workflows. Methods: A cross-sectional study utilised data from two National Health and Nutrition Examination Survey (NHANES) cycles (2011–2014). Logistic regression was applied to develop the prediction model among 1870 dentate adults aged 30–50, a critical onset period for oral diseases. The primary outcome was derived from objective oral examinations conducted by licensed dentists. The resulting questionnaire-based tool provides two recommendations: a visit to a dentist or continuation of routine oral care. Results: External validation using the independent NHANES 2013–2014 cohort (n = 2024) demonstrated robust and clinically relevant predictive capacity, with an AUC of 0.822 (95% CI 0.803–0.842). The model exhibited acceptable calibration (slope 0.85; intercept 0.04) and stable operation. Decision-curve analysis showed net benefit across thresholds; at Pt = 0.33, 14/100 unnecessary screenings were avoided compared with treat-all. Internal validation using a 10% hold-out partition (n = 184) supported these findings, indicating similarly strong discrimination (AUC 0.817, 95% CI 0.751–0.884). Conclusions: Early midlife is a consistent and informative period for oral health intervention. A scalable 14-predictor architecture using accessible indicators established high clinical utility, supporting integration into digital health and primary care. This approach enables efficient population-level prevention. Large-scale, nationally representative NHANES data allowed for the investigation of less-studied factors, including intermittent smoking, metabolic dysregulation, and elevated cholesterol levels.

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