An evaluation of quantitative percussion diagnostics for determining the probability of a microgap defect in restored and unrestored teeth: A prospective clinical study.

STATEMENT OF PROBLEM: Current dental diagnostics are image based and cannot detect a structural microgap defect such as a crack in a tooth. Whether percussion diagnostics can effectively diagnose a microgap defect is unclear. PURPOSE: The purpose of the present study was to determine from a large multicenter prospective clinical study whether quantitative percussion diagnostics (QPD) could detect structural damage in teeth and whether a probability of its presence could be provided. MATERIAL AND METHODS: A nonrandomized prospective and multicenter clinical validation study with 224 participants was performed in 5 centers with 6 independent investigators. The study used QPD and the normal fit error to determine whether a microgap defect was present in a natural tooth. Teams 1 and 2 were blinded. Team 1 tested teeth scheduled for restoration with QPD, and Team 2 disassembled the teeth aided by a clinical microscope, transillumination, and a penetrant dye. Microgap defects were documented in written and video formats. Controls were participants without damaged teeth. The percussion response from each tooth was stored on a computer and analyzed. A total of 243 teeth were tested to provide approximately 95% power to test the performance goal of 70%, based on an assumed population overall agreement of 80%. RESULTS: Regardless of the collection method, tooth geometry, restoration material used, or restoration type, the data on detecting a microgap defect in a tooth were accurate. The data also reflected good sensitivity and specificity consistent with previously published clinical studies. The combined study data showed an overall agreement of 87.5% with a 95% confidence interval (84.2 to 90.3), beyond the 70% predetermined performance goal. The combined study data determined whether it was possible to predict the probability of a microgap defect. CONCLUSIONS: The results showed that the data on detecting microgap defects in a tooth site were consistently accurate and confirmed that QPD provided information to aid the clinician in treatment planning and early preventative treatment. QPD can also alert the clinician of probable diagnosed and undiagnosed structural problems via the use of a probability curve.

General Complications after Endoscopic Sinus Surgery in Smokers: A 2005-2016 NSQIP Analysis.

BACKGROUND: Exposure to cigarette smoke has been associated with a higher incidence of postoperative complications across a variety of surgical specialties. However, it is unclear if smoking increases this risk after endoscopic sinus surgery (ESS). Because complication rates after ESS are relatively low, a large national database allows for a statistically meaningful study of this topic. METHODS: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) dataset from 2005 to 2016 was analyzed. Patients who underwent ESS were identified. Thirty-day postoperative complication rates between smokers and nonsmokers were compared. Complications included infection, thromboembolic events, reintubation, readmission, acute renal failure, and cardiovascular events. RESULTS: 921 patients who underwent ESS were identified. 182 (20%) were smokers and 739 (80%) were nonsmokers. 609 patients underwent outpatient ESS, while 312 patients underwent inpatient ESS. A total of 12 patients experienced postoperative surgical site infections involving the deeper tissues beyond the wound (organ/space SSI). On univariate analysis, smoking was associated with a higher incidence of organ/space SSI (P = .0067) and pulmonary embolism (P = .0321) after ESS. On multivariate logistic regression, smoking was associated with increased odds (4.495, 1.11- 8.17, P = .0347) of organ/space SSI after ESS. CONCLUSIONS: This study demonstrates an association between exposure to cigarette smoke and potentially serious surgical site infections in the 30-day postoperative period after ESS. Our findings may help when counseling smokers who are considering ESS. Further study is required to understand the nature of these infections and ways to prevent them.Level of Evidence: 2c ("health outcomes").

Improving life sciences information retrieval using semantic web technology.

The ability to retrieve relevant information is at the heart of every aspect of research and development in the life sciences industry. Information is often distributed across multiple systems and recorded in a way that makes it difficult to piece together the complete picture. Differences in data formats, naming schemes and network protocols amongst information sources, both public and private, must be overcome, and user interfaces not only need to be able to tap into these diverse information sources but must also assist users in filtering out extraneous information and highlighting the key relationships hidden within an aggregated set of information. The Semantic Web community has made great strides in proposing solutions to these problems, and many efforts are underway to apply Semantic Web techniques to the problem of information retrieval in the life sciences space. This article gives an overview of the principles underlying a Semantic Web-enabled information retrieval system: creating a unified abstraction for knowledge using the RDF semantic network model; designing semantic lenses that extract contextually relevant subsets of information; and assembling semantic lenses into powerful information displays. Furthermore, concrete examples of how these principles can be applied to life science problems including a scenario involving a drug discovery dashboard prototype called BioDash are provided.

BioDash: a Semantic Web dashboard for drug development.

A researcher's current scientific understanding is assembled from multiple sources of facts and knowledge, along with beliefs and hypotheses of their interpretations. A comprehensive and structured aggregation of all the relevant components is to-date not possible using standard database technologies, nor is it obvious how to include beliefs, such as models and hypotheses into such a bundle. When such information is required as the basis for important decision-making (e.g., in drug discovery), scientists often resort to using commercial presentation applications. This is sub-optimal for the effective use of knowledge, and alternatives that support the inclusion of meaning are urgently needed. This paper describes a prototype Semantic Web application, BioDash, which attempts to aggregate heterogeneous yet related facts and statements (using an RDF model) into an intuitive, visually descriptive and interactive display.