Internal adaptation of fixed prosthodontics at shoulder preparation level evaluated by horizontal sections

AIM: The precision of fixed prosthodontic restorations is fundamental for clinical success: well-fitting crowns reduce the risk of recurrent caries and periodontal disease. The aim of this study is to evaluate the internal fit of fixed prosthodontics at the shoulder preparation level by examining horizontal sections. METHODS: Twenty-four extracted teeth were resin-embedded and prepared on the platform of an iso-parallelometer with a 90 degrees shoulder with a rounded internal angle. Auro Galva Crown (AGC) copings were cemented in place. The preparations were observed by 3 different assessors at 8 points, first externally and then internally at 2 levels by grinding the specimen perpendicular to the long axis at 0.5 mm and at 0.2 mm from the margin of the preparation. A correction factor was calculated to derive real values from measured values. The results were analyzed using a linear regression with robust standard errors, accounting for within-subject correlation introduced by multiple measurements. Shrout-Fleiss Intraclass Correlation Coefficient (ICC) for Inter-Rater Reliability were calculated at each stage. RESULTS: Internal measurements at 0.5 and 0.2 mm from the margin provided data similar to the external margin data. Average inter-assessor differences were in the range of 2 mm. ICC ranged from 0.93 for the 0.5 mm level to 0.97 for the external level. CONCLUSIONS: External measurements effectively predict the internal precision at the shoulder level. Horizontal perimarginal sections allow the fit to be studied through the evaluation of a great number of points. Traditional vertical sections for the evaluation of internal fit enable only a few points to be observed. This internal observation method may be suitable for testing new materials

Empirical assessment of gamma generalized linear mixed models for outcomes in oral health research

Objectives. In case of positive and skewed data, the most common approach is to fit a linear model to log-transformed data, with the parameters being eventually evaluated after a back-transformation on the original scale. This method is known to be biased, in particular in repeated measurement studies, with the bias increasing with the heterogeneity in data. An alternative approach based on the Generalized Linear Mixed Model (GLMM) is therefore hereby proposed. Methods. We provide evidence on the performance of the Gamma GLMM model under a variety of data generating mechanisms and compare it to that of the Linear Mixed Effect Model (log-LME) on a log-transformed response. Three case studies from fixed prosthodontics are analyzed and discussed. Results. When the error variance is constant, log-LME and Gamma GLMM produce similar estimates with a negligible relative bias. In presence of heteroscedasticity, the log-LME for a Gamma response provides a substantially biased estimate of the true value, increasing as the error variance increases. Conclusions. No single alternative is best under all the conditions examined in this paper. However, the gamma regression model with a log link seems to be more robust to alternative data generating mechanisms than either log-LME

Flexibility of Bayesian generalized linear mixed models for oral health research.

Many outcome variables in oral research are characterized by positive values and heavy skewness in the right tail. Examples are provided by many distributions of dental variables such as DMF (decayed, missing, filled teeth) scores, oral health impact profile score, gingival index scores, and microbiologic counts. Moreover, heterogeneity in data arises when more than one tooth is studied for each patient, due to the clusterization.Over the past decade, linear mixed models (LMEs) have become a common statistical tool to account for within-subject correlation in data with repeated measures. When a normal error is reasonably assumed, estimates of LMEs are supported by many statistical packages. Such is not the case for skewed data, where generalized linear mixed models (GLMMs) are required. However, the current software available supports only special cases of GLMMs or relies on crude Laplace-type approximation of integrals. In this study, a Bayesian approach is taken to estimate GLMMs for clustered skewed dental data. A Gamma GLMM and a log-normal model are employed to allow for heterogeneity across clusters, deriving from the patient-operator-tooth susceptibility typical of this clinical context. A comparison to the frequentist framework is also provided. In our case, Gamma GLMM fits data better than the log-normal distribution, while providing more precise estimates compared with the likelihood approach. A key advantage of the Bayesian framework is its ability to readily provide a flexible approach for implementation while simultaneously providing a formal procedure for solving inference problems