Background and Objective The onset and progression of early tooth decay is often preventable with dental sealants. However, occasionally decay progresses underneath the sealant. Current technology does not permit monitoring of potential lesion progression or arrest. Dental sealants themselves mask the visual cues that identify early tooth decay, and radiographs are not sufficiently sensitive. Therefore clinicians can be reluctant to use dental sealant. The objective of this ex vivo study was to evaluate the ability of dentists to detect decay beneath commonly used dental sealants using Optical Coherence Tomography (OCT) imaging. Study Designs/Materials/Methods Forty extracted teeth were divided into equal groups of carious and non-carious teeth, as determined by visual inspection. After radiographs and OCT imaging, teeth were randomly assigned for sealant placement with one of four commonly purchased dental sealants: Clinpro™, Fuji Triage™, Embrace Wet Bond™, and Delton™. Following sealant placement, teeth were radiographed, imaged with OCT, sectioned, examined histologically, and scored as healthy/not healthy. OCT and radiographic images were scored separately. The gold standard was histopathological diagnosis from the serial sections. Cohen’s Kappa, sensitivity, negative predictive value and positive predictive value were computed for all measures. Results After 90 mins training, pre-standardized dentists were able to detect tooth decay more accurately using OCT than with visual or radiographic examination. Detection using OCT was somewhat better prior to sealant placement than afterwards. This effect varied in size depending on the type of sealant used. Radiographic diagnosis was also less accurate after sealant placement. Of the four dental sealants, Delton provided excellent positive predictive value and the best post-sealant negative predictive values. Conclusion In this ex vivo study, dentists were able to detect tooth decay beneath four commonly used dental sealants based on OCT images. Clinical investigations are now underway to determine the usefulness of this approach in vivo.

We applied a discourse analysis (DA) to the electronic chat room discussions of a 16-week, Internet-based section of a class in statistical methods in psychology. This analysis revealed that across the semester, several DA categories (e.g., total number of student comments) were correlated with final grade in the class. An additional analysis involving only the chat room discussion of Week 3 revealed that 2 DA categories (i.e., student response to a problem or example given in lecture and total number of student comments) correlated with final grade in the class. We discuss the pedagogical implication of these results with regard to an instructor's ability to identify early warning predictors of student performance in the virtual classroom.This study explored the possibility that a discourse analysis (DA) of the real-time (i.e., synchronous) communication that occurs in electronic chat rooms would reveal predictors of students' performance in the virtual classroom. Because Internet technology and courseware are new, there are only a few studies on the characteristics and behaviors of students who succeed in Internet-based classes. Indeed, a recent review of distance learning research revealed that most studies were anecdotal in nature and only a few involved courses taught via the Internet (Phipps & Merisotis, 1999). For pedagogical reasons the lack of empirical research is unfortunate because the effective design of flexible learning environments that is possible in a technology-rich environment is hampered without an understanding of the characteristics, attitudes, and needs of students (Smith, 1997). Consequently, course design may become technology driven rather than employing technology as a resource in support of student needs (Trapp, Hammond, & Bray, 1996).A recent report (McCollum, 1997) is illustrative of a research area that is primarily anecdotal in nature. It described two sections of a statistics course offered by a sociology instructor: one in the conventional format and the second as an online Internet-based course. The instructor randomly assigned students to one of these two sections and reported that students in the virtual classroom collaborated more than students in the conventional classroom. Interviews with the Web-based students revealed an overall positive impression of the egalitarian and nonintimidating characteristics of electronic chat rooms. However, this study did not report data on academic performance or the type of communication that occurred in chat room discussions. Another study reported that honors students taking an Internet-based statistics course had positive perceptions of the course (Varnhagen, Drake, & Finley, 1997). Once again, the academic performance of these students was not reported.This study involved students who chose to register for an Internet-based section of a class in statistical methods in psychology rather than conventional sections of the class. Consequently, students retrieved all course materials (syllabus, quizzes, assignments, etc.) from the course ho...

Case management programs are commonly offered by health plans, hospitals, freestanding case management vendors, and others to various purchasers of these services. Case management programs are materially different from standard medical management or disease management programs in a number of ways. The patients have complex medical conditions combined with many other variables that tend to increase their costs and patterns of utilization. Purchasers of case management services frequently demand some form of performance guarantees as evidence that these case management programs are reducing utilization and medical costs. Inherent challenges of high variation in clinical and cost characteristics of the case management population make it difficult to develop standard return on investment performance guarantees. There are, however, other methodologies and statistical approaches to measure and evaluate program performance. We seek to outline major limiting issues that differentiate case management financial impact analyses from other clinical programs, and to define a framework for beginning a dialogue between suppliers and purchasers of these services to create a program value proposition.

Healthcare has become a data-intensive business. Over the last 30 years, we have seen significant advancements in the areas of health information technology and health informatics as well as healthcare modeling and artificial intelligence techniques. Health informatics, which is the science of health information,1 has made great progress during this period (American Medical Informatics Association). Likewise, data mining, which has been generally defined as the application of technology and statistical/mathematical methods to uncover relationships and patterns between variables in data sets, has experienced noteworthy improvements in computer technology (e.g., hardware and software) in addition to applications and methodologies (e.g., statistical and biostatistical techniques such as neural networks, regression analysis, and classification/segmentation methods) (Kudyba & Hoptroff, 2001). Though health informatics is a relatively young science, the impact of this area on the health system and health information technology industry has already been seen, evidenced by improvements in healthcare delivery models, information systems, and assessment/diagnostic tools.