Objectives: To examine the influence of the decision-making algorithms published by Tonetti and Sanz in 2019 on the diagnostic accuracy in two differently experienced groups of dental students using the current classification of periodontal diseases.
Materials and methods: Eighty-three students of two different clinical experience levels were randomly allocated to control and study group, receiving the staging and grading matrix, resulting in four subgroups. All diagnosed two patient cases with corresponding periodontal charts, panoramic radiographs, and intraoral photographs. Both presented severe periodontal disease (stage III, grade C) but considerably differed in complexity and phenotype according to the current classification of periodontal diseases. Controls received the staging and grading matrix published within the classification, while study groups were additionally provided with decision-trees published by Tonetti and Sanz. Obtained data was analyzed using chi-square test, Spearman's rank correlation, and logistic regression.
Results: Using the algorithms significantly enhanced the diagnostic accuracy in staging (p = 0.001*, OR = 4.425) and grading (p < 0.001**, OR = 30.303) regardless of the clinical experience. In addition, even compared to the more experienced control, less experienced students using algorithms showed significantly higher accuracy in grading (p = 0.020*). No influence on the criteria extent could be observed comparing study groups to controls.
Conclusion: The decision-making algorithms may enhance diagnostic accuracy in dental students using the current classification of periodontal diseases.
Clinical relevance: The investigated decision-making algorithms significantly increased the diagnostic accuracy of differently experienced under graduated dental students and might be beneficial in periodontal education.
Keywords: Clinical decision-making; Decision trees; Dental education; Periodontics.
© 2023. The Author(s).