IntroductionThe aim of this study was to understand the trends of teaching Computer Aided Design/Computer Aided Manufacturing (CAD/CAM) in the undergraduate dental curriculum in the MENA region by conducting an online survey among the undergraduate dental colleges in this region.Materials and MethodsAn online survey was conducted using Google Forms consisting of 20 questions that could be answered by “yes” or “no” responses, multiple‐choice answers or in a descriptive, “open” format. A total of 55 participants from the MENA region representing their dental college were requested to participate in this study.ResultsThe response rate of the survey was 85.5% following the twofold follow‐up reminders method. Although the vast majority of professors demonstrated vital knowledge in the practical use of CAD/CAM, the majority of them did not provide theoretical and practical training on CAD/CAM in their institutions. Among the schools with established levels of teaching CAD/CAM, nearly 50% of them provide both pre‐clinical and clinical training on CAD/CAM. Despite the availability of extra‐curricular training courses on CAD/CAM outside the university setting, there is a lack of advocating for students to enrol in those courses by the institutions. More than 80% of the participants agreed that CAD/CAM has a strong future in chair‐side dental clinics and CAD/CAM needs to be incorporated within undergraduate studies.ConclusionBased on the results of the current study, it is understandable that an intervention is required by the dental education providers to cope with the growing demand for CAD/CAM technology for the current and future dental practitioners of the MENA region.

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3 (HUI3) is a generic multiattribute preference-based measure of health status and health-related quality of life that is widely used as an outcome measure in clinical studies, in population health surveys, in the estimation of quality-adjusted life years. We aimed to present a multi-attribute utility function and eight single-attribute utility functions for the HUI3 system based on community preferences in Japan. Methods: Two preference surveys were conducted. One, the modeling survey, collected preference scores for the estimation of the utility functions. The other, the direct survey, provided independent scores to assess the predictive validity of the utility functions. Preference measures included value scores obtained on the Feeling Thermometer and standard gamble utility scores obtained using the Chance Board. We recruited 1,043 respondents (aged 20-79) from the general population, stratified by gender and age group, from five Japanese cities. Results: Estimates were obtained for eight single-attribute utility functions and an overall multi-attribute utility function (MAUF). The Japanese HUI3 MAUF is u=1.016*(b1*b2*b3*b4*b5*b6*b7*b8)-0.016. The minimum predicted multi-attribute utility score was-0.002. The intraclass correlation coefficient between directly measured utility scores and scores generated by the multi-attribute function for 53 health states was 0.742. Conclusions: The HUI3 scoring function was developed in Japan has strong theoretical and empirical foundations. It seems to perform well in predicting directly measured scores for health technology assessment in Japan.

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