Name
Title | ||
|---|---|---|
Prediction And Failure Analysis Of Composite Resin Restorations In The Posterior Sector Applied In Teaching Dental Students |
Abstract | ||
|---|---|---|
The failure of composite resin restorations in the posterior region is an ongoing concern in current clinical practice This study assesses possible factors and causes of the failure of restoration 1 year after their placement by fourth year dental students (on a 5-year degree course). While the systematic assessment of dental students does not appear to have received much attention in the field, this study asserts the need and benefit of an assessment methodology that can (1) predict the success or failure of restorations placed by dental students and (2) assist the clinical instructor in identifying the performance profile of each student. Eighty-one patients aged 26-77 years were treated by 81 undergraduates in a prospective cohort study from November 2013 to December 2015. One year after treatment, restorations were assessed by the same staff member who acted as the supervisor during the restoration placement. A CBR system was applied to make predictions about restorations. The CBR includes different machine learning techniques and statistical tests in the CBR cycle. The system calculates the relevant variables, which are used to predict failures. The accuracy of the system is measured with the AUC and the accuracy. The AUC obtained is 0.935 while the Kappa index and the accuracy are 91.36 and 0.75, respectively. In conclusion, factors related to the patient and to the treatment are associated to the failure of the restorative treatment. Of particular interest, the CBR was useful for the performance of a predictive model to estimate the probability of failure of resin restorations placed by students. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s12652-020-01804-7 | JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING |
Keywords | DocType | Volume |
Dental caries, Failure, Competency based education, Assessment, CBR, Classifiers, Statistical test | Journal | 11 |
Issue | ISSN | Citations |
11 | 1868-5137 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ignacio Aliaga | 1 | 1 | 1.76 |
| Juan F. De Paz | 2 | 0 | 0.34 |
| Vicente Vera | 3 | 27 | 5.45 |
| Alvaro Enrique Garcia | 4 | 19 | 3.92 |
| Javier Bajo | 5 | 1451 | 118.96 |