20 research outputs found

    Dental School “Research Publication Distribution” in Dental Versus Non-Dental Journals

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    http://deepblue.lib.umich.edu/bitstream/2027.42/101139/1/2007-IADR-Poster-Bayne.pd

    Cyclic Loading Effect on Color Stability of Unshaded versus Shaded Zirconia

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    Statement of Problem Shaded versions of dental zirconia may improve initial color matching to teeth, but might change color with cyclic mechanical loading. Purpose The purpose of this study was to measure the color of unshaded and shaded zirconia dental ceramic before and after cyclic mechanical loading and calculate color differences (ΔE). Material and Methods Disk-shaped specimens (N = 30, Nobel Procera, 0.8 mm thick, 12 mm diameter) of unshaded or shaded zirconia (intrinsically shaded by small oxide modifications) were fabricated by the Nobel Biocare using standard CAD-CAM processing. Milled surfaces were polished. CIE L∗a∗b∗ values were measured (Konica Minolta spectrophotometer) before and after mechanical cycling (custom modified Leinfelder test machine, biaxial flexure loading, load = 80 N × 500,000 cycles, dry), and L∗a∗b∗ individual differences and ΔE color differences were calculated and compared (ANOVA, p < 0.05). Results Mean L∗a∗b∗ values for the unshaded group before (U1 = 86.165, -0.887, 0.372) and after (U2 = 84.860, -0.805, 0.097) cyclic loading were compared to the shaded group before (S1 = 75.281, -0.679, 23.251) and after (S2 = 74.961, -1.233, 22.439) cyclic loading. All color variables for both unshaded and shaded groups were significantly different between before and after cyclic loading (p < .004) except for the L∗ value of the shaded group. The ΔE for unshaded (1.441 ± 0.495) versus shaded (1.252 ± 0.363) were statistically different but clinically the change would not be detectable at this point. Conclusions The color of the unshaded and shaded zirconia specimens was influenced by cyclic loading (p < 0.05). Color changes were detectable but small at levels up to 500,000 cycles, and remained clinically acceptable at that point. Clinical Significance It is important to acknowledge any possible color changes that might occur in zirconia restorations, especially in the esthetic zone. Minor color changes that are individually imperceptible to the human eye within different restorative components may be compounded to produce clinically significant color change that is not aesthetically acceptable. © 2015 Wiley Periodicals, Inc

    Framework for e-learning assessment in dental education: a global model for the future

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    The framework presented in this article demonstrates strategies for a global approach to e-curricula in dental education by considering a collection of outcome assessment tools. By combining the outcomes for overall assessment, a global model for a pilot project that applies e-assessment tools to virtual learning environments (VLE), including haptics, is presented. Assessment strategies from two projects, HapTEL (Haptics in Technology Enhanced Learning) and UDENTE (Universal Dental E-learning), act as case-user studies that have helped develop the proposed global framework. They incorporate additional assessment tools and include evaluations from questionnaires and stakeholders' focus groups. These measure each of the factors affecting the classical teaching/learning theory framework as defined by Entwistle in a standardized manner. A mathematical combinatorial approach is proposed to join these results together as a global assessment. With the use of haptic-based simulation learning, exercises for tooth preparation assessing enamel and dentine were compared to plastic teeth in manikins. Equivalence for student performance for haptic versus traditional preparation methods was established, thus establishing the validity of the haptic solution for performing these exercises. Further data collected from HapTEL are still being analyzed, and pilots are being conducted to validate the proposed test measures. Initial results have been encouraging, but clearly the need persists to develop additional e-assessment methods for new learning domains
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