Bond strength of adhesives to dentin contaminated with smoker's saliva.

The purpose of this study was to determine the effects of contamination with smoker's and non-smoker's saliva on the bond strength of resin composite to superficial dentin using different adhesive systems. The interfacial structure between the resin and dentin was evaluated for each treatment using environmental scanning electron microscopy (ESEM). Freshly extracted human molars were ground with 600-grit SiC paper to expose the superficial dentin. Adhesives [One-Up-Bond-F-Plus (OUFP) and Adper-Prompt-L-Pop (APLP)] and resin composite (TPHSpectrum) were bonded to the dentin (n = 8/group, 180 total specimens) under five surface conditions: control (adhesive applied following manufacturers' instructions); saliva, then 5-s air dry, then adhesive; adhesive, saliva, 5-s air dry; adhesive, saliva, 5-s water rinse, 5-s air dry (ASW group); and adhesive, saliva, 5-s water rinse, 5-s air dry, reapply adhesive (ASWA group). After storage in water at 37 degrees C for 24 h, the specimens were debonded under tension at a speed of 0.5 mm/min. ESEM photomicrographs of the dentin/adhesive interfaces were taken. Mean bond strength ranged from 8.1 to 24.1 MPa. Fisher's protected least significant difference (P = 0.05) intervals for critical adhesive, saliva, and surface condition differences were 1.3, 1.3, and 2.1 MPa, respectively. There were no significant differences in bond strength to dentin between contamination by smoker's and nonsmoker's saliva, but bond strengths were significantly different between adhesive systems, with OUFP twice as strong as APLP under almost all conditions. After adhesive application and contamination with either smoker's or nonsmoker's saliva followed by washing and reapplication of the adhesive (ASWA group), the bond strength of both adhesive systems was the same as that of the control group.

Comparison of accuracy captured by different controlled languages in oral pathology diagnoses.

This project was comparing the accuracy of capturing the oral pathology diagnoses among different coding systems. 55 diagnoses were selected for comparison among 5 coding systems. The results of accuracy in capturing oral diagnoses are: AFIP (96.4%), followed by Read 99 (85.5%), SNOMED 98 (74.5%), ICD-9 (43.6%), and CDT-3 (14.5%). It shows that the currently used coding systems, ICD-9 and CDT-3, were inadequate, whereas the AFIP coding system captured the majority of oral diagnoses. In conclusion, the most commonly used medical and dental coding systems lack terms for the diagnosis of oral and dental conditions.

DEVELOPING A METHOD FOR IDENTIFYING AND REDUCING FUNCTIONAL DISCREPANCIES OF INFORMATION SYSTEMS

Currently more than half of Electronic Health Record (EHR) projects fail. Most of these failures are not due to flawed technology, but rather due to the lack of systematic considerations of human issues. Among the barriers for EHR adoption, function mismatching among users, activities, and systems is a major area that has not been systematically addressed from a human-centered perspective. A theoretical framework called Functional Framework was developed for identifying and reducing functional discrepancies among users, activities, and systems. The Functional Framework is composed of three models – the User Model, the Designer Model, and the Activity Model. The User Model was developed by conducting a survey (N = 32) that identified the functions needed and desired from the user’s perspective. The Designer Model was developed by conducting a systemic review of an Electronic Dental Record (EDR) and its functions. The Activity Model was developed using an ethnographic method called shadowing where EDR users (5 dentists, 5 dental assistants, 5 administrative personnel) were followed quietly and observed for their activities. These three models were combined to form a unified model. From the unified model the work domain ontology was developed by asking users to rate the functions (a total of 190 functions) in the unified model along the dimensions of frequency and criticality in a survey. The functional discrepancies, as indicated by the regions of the Venn diagrams formed by the three models, were consistent with the survey results, especially with user satisfaction. The survey for the Functional Framework indicated the preference of one system over the other (R=0.895). The results of this project showed that the Functional Framework provides a systematic method for identifying, evaluating, and reducing functional discrepancies among users, systems, and activities. Limitations and generalizability of the Functional Framework were discussed.