Longevity of Crown Margin Repairs Using Glass Ionomer Cement: A Retrospective Study.

OBJECTIVE: The objective of this study was to determine the survival time of crown margin repairs (CMRs) with glass ionomer and resin-modified glass ionomer cements on permanent teeth using electronic dental record (EDR) data. METHODS: We queried a database of EDR (axiUm; Exan Group, Coquitlam, BC, Canada) in the Indiana University School of Dentistry (IUSD), Indianapolis, IN, USA, for records of patients who underwent CMRs of permanent teeth at the Graduate Operative Dentistry Clinic. Two examiners developed guidelines for reviewing the records and manually reviewed the clinical notes of patient records to confirm for CMRs. Only records that were confirmed with the presence of CMRs were retained in the final dataset for survival analysis. Survival time was calculated by Kaplan-Meier statistics, and a Cox proportional hazards model was performed to assess the influence of age, gender, and tooth type on survival time (a<0.05). RESULTS: A total of 214 teeth (115 patients) with CMR were evaluated. Patient average age was 69.4 ± 11.7 years old. Posterior teeth accounted for 78.5% (n=168) of teeth treated. CMRs using glass ionomer cements had a 5-year survival rate of 62.9% and an annual failure rate (AFR) of 8.9%. Cox proportional-hazards model revealed that none of the factors examined (age, gender, tooth type) affected time to failure. CONCLUSION: The results indicate the potential of CMRs for extending the functional life of crowns with defective margins, thus reducing provider and patient burden of replacing an indirect restoration. We recommend future studies with a larger population who received CMR to extend the generalizability of our findings and to determine the influence of factors such as caries risk and severity of defects on survival time.

Does Nonsurgical Periodontal Treatment Improve Systemic Health?

Clinicians frequently stress the importance of maintaining good oral health for multiple reasons, including its link to systemic health. Because periodontal treatment reduces inflammation in oral tissues, some hypothesize it may positively affect systemic outcomes by reducing inflammation in the body. A significant number of systematic reviews (SRs) and meta-analyses (MAs) have evaluated the effect of periodontal treatment on systemic outcomes. However, inconsistent findings and questionable methodological rigor make drawing conclusions difficult. We conducted a systematic review of reviews that studied the effect of nonsurgical periodontal treatment on systemic disease outcomes. We report on outcomes evaluated, categorizing them as biomarkers, and surrogate or clinical endpoints. In addition, we used A MeaSurement Tool to Access systematic Reviews 2 (AMSTAR 2) to evaluate the methodological quality of the reviews. Of the 52 studies included in our review, 21 focused on diabetes, 15 on adverse birth outcomes, 8 on cardiovascular disease, 3 each on obesity and rheumatoid arthritis, and 2 on chronic kidney disease. Across all studies, surrogate endpoints predominated as outcomes, followed by biomarkers and, rarely, actual disease endpoints. Ninety-two percent of studies had "low" or "critically low" AMSTAR 2 confidence ratings. Criteria not met most frequently included advance registration of the protocol, justification for excluding individual studies, risk of bias from individual studies being included in the review, and appropriateness of meta-analytical methods. There is a dearth of robust evidence on whether nonsurgical periodontal treatment improves systemic disease outcomes. Future reviews should adhere more closely to methodological guidelines for conducting and reporting SRs/MAs than has been the case to date. Beyond improved reviews, additional rigorous research on whether periodontal treatment affects systemic health is needed. We highlight the potential of large-scale databases containing matched medical and dental record data to inform and complement future clinical research studying the effect of periodontal treatment on systemic outcomes.

Effectiveness of smart phone application use as continuing medical education method in pediatric oral health care: a randomized trial.

BACKGROUND: Continuing education aims at assisting physicians to maintain competency and expose them to emerging issues in their field. Over the last decade, approaches to the delivery of educational content have changed dramatically as medical education at all levels is now benefitting from the use of web-based content and applications for mobile devices. The aim of the present study is to investigate through a randomized trial the effectiveness of a smart phone application to increase public health service physicians' (PHS physicians) knowledge regarding pediatric oral health care. METHOD: Five of all seven DHCs (District Health Center) in Tehran, which were under the supervision of Tehran University of Medical Sciences and Iran University of Medical Sciences, were selected for our study. Physicians of one DHC had participated in a pilot study. All PHS physicians in the other four centers were invited to the current study on a voluntary basis (n = 107). They completed a self-administered questionnaire regarding their knowledge, attitudes, practice in pediatric dentistry, and background. PHS physicians were assigned randomly to intervention and control groups; those in the intervention group, received a newly designed evidence-based smartphone application, and those in the control group received a booklet, a CME seminar, and a pamphlet. A post-intervention survey was administered 4 months later and t-test and repeated measures ANCOVA (Analysis of Covariance) were performed to measure the difference in the PHS physicians' knowledge, attitude and practice. RESULTS: In both groups, the mean knowledge scores were significantly higher (p-Value < 0.001) in post-intervention data compared to those at baseline. Similar results existed in attitude and practice scores. Although the scores in knowledge in the intervention group indicating potentially greater improvement when compared to those of the control group, the differences between the two groups were not statistically significant (dif: 0.84, 95% CI - 0.35 to 2.02). CONCLUSION: In the light of the limitations of the present study, smart phone applications could improve knowledge, attitude and practice in physicians although this method was not superior to the conventional method of CME. TRIAL REGISTRATION: Our clinical trial had been registered in Iranian Registry of Clinical Trials (registration code: IRCT2016091029765N1).

Leveraging Electronic Dental Record Data to Classify Patients Based on Their Smoking Intensity.

BACKGROUND: Smoking is an established risk factor for oral diseases and, therefore, dental clinicians routinely assess and record their patients' detailed smoking status. Researchers have successfully extracted smoking history from electronic health records (EHRs) using text mining methods. However, they could not retrieve patients' smoking intensity due to its limited availability in the EHR. The presence of detailed smoking information in the electronic dental record (EDR) often under a separate section allows retrieving this information with less preprocessing. OBJECTIVE: To determine patients' detailed smoking status based on smoking intensity from the EDR. METHODS: First, the authors created a reference standard of 3,296 unique patients' smoking histories from the EDR that classified patients based on their smoking intensity. Next, they trained three machine learning classifiers (support vector machine, random forest, and naïve Bayes) using the training set (2,176) and evaluated performances on test set (1,120) using precision (P), recall (R), and F-measure (F). Finally, they applied the best classifier to classify smoking status from an additional 3,114 patients' smoking histories. RESULTS: Support vector machine performed best to classify patients into smokers, nonsmokers, and unknowns (P, R, F: 98%); intermittent smoker (P: 95%, R: 98%, F: 96%); past smoker (P, R, F: 89%); light smoker (P, R, F: 87%); smokers with unknown intensity (P: 76%, R: 86%, F: 81%), and intermediate smoker (P: 90%, R: 88%, F: 89%). It performed moderately to differentiate heavy smokers (P: 90%, R: 44%, F: 60%). EDR could be a valuable source for obtaining patients' detailed smoking information. CONCLUSION: EDR data could serve as a valuable source for obtaining patients' detailed smoking information based on their smoking intensity that may not be readily available in the EHR.