Statement Of Problem: Reports on digital complete dentures (CDs) are increasing. However, systematic reviews on their accuracy and influencing factors are lacking.
Purpose: The purpose of this systematic review was to evaluate the accuracy of digital CDs and to summarize influencing factors.
Material And Methods: An electronic search of the English language literature from January 2009 to October 2019 was performed in the database PubMed/MEDLINE, with the results enriched by manual searches and citation mining. Factors investigated in the selected articles included the fabrication technique, type of computer-aided design and computer-aided manufacturing (CAD-CAM) system, shape of reference model, long-term service, analytical method, and statistical indicators.
Results: A total of 522 articles were identified, of which 14 in vitro articles met the inclusion criteria. Eight articles compared the adaptation of the denture base between digital and conventional methods, 4 studies evaluated the occlusal discrepancies, 4 compared the trueness or adaptation of the denture fabricated with CAD-CAM milling and 3D printing, 1 compared the denture adaptation with 4 different CAD-CAM systems, and 2 evaluated the adaptation of the denture base before and after incubation in artificial saliva.
Conclusions: Most of the studies reported clinically acceptable values for the occlusal trueness and adaptation of digital CDs. The digital CDs showed similar or better adaptation than conventionally fabricated CDs, and the greatest misfit of the intaglio surface was reported in the posterior palatal seal area and border seal area. The fabrication technique, CAD-CAM system, and long-term service were statistically significant in relation to denture accuracy. Clarification is needed concerning the accuracy of digital CDs according to the shape of the cast, the parameters related to the CAD-CAM process, the analytical method, and the statistical indicators. No clear conclusions can be drawn about the superiority of CAD-CAM milling and 3D printing regarding denture accuracy.
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http://dx.doi.org/10.1016/j.prosdent.2020.01.004 | DOI Listing |
EBioMedicine
December 2024
Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh Medical Center (UPMC). Electronic address:
Background: We hypothesised that the implementation of an electronic medical record (EMR) embedded perioperative clinical decision support (CDS) application, Anesthesia Testing Guidelines (ATG), would result in at least a 10% reduction of unnecessary perioperative testing in patients undergoing elective surgeries.
Methods: The development and implementation of ATG occurred in several phases: 1) team development, 2) development of an embedded EMR application, 3) creation of ATG training and education toolkit, and 4) implementation involving promoting ATG through training and education, addressing challenges, and monitoring compliance. The proportions of patients with any overutilisation across 19 perioperative tests were compared between the baseline cohort and the ATG implementation cohort.
JAMIA Open
December 2024
University of Massachusetts Chan Medical School, Departments of Medicine, Worcester, MA 01655, United States.
Objectives: Many routine patient care items should be reviewed at least daily for intensive care unit (ICU) patients. These items are often incompletely performed, and dynamic clinical decision support tools (CDSTs) may improve attention to these daily items. We sought to evaluate the accuracy of institutionalized electronic health record (EHR) based custom dynamic CDST to support 22 ICU rounding quality metrics across 7 categories (hypoglycemia, venothromboembolism prophylaxis, stress ulcer prophylaxis, mechanical ventilation, sedation, nutrition, and catheter removal).
View Article and Find Full Text PDFAnesthesiology
November 2024
Department of Anesthesiology, Yale School of Medicine, New Haven, CT.
Background: Antibiotics play a crucial role in preventing surgical site infections, yet adherence to Infectious Disease Society of America (IDSA) guidelines varies widely. This qualitative study aimed to explore factors influencing perioperative antibiotic administration and assess the potential impact of a clinical decision support (CDS) tool on guideline adherence.
Methods: In this qualitative study, perioperative personnel with diverse roles (surgeons, anesthesiologists, certified nurse anesthetists, trainees, and pharmacists) were interviewed using a semi-structured interview format from September 2023 through April 2024.
Diabetologia
November 2024
Service of Endocrinology, Diabetology, Nutrition and Therapeutic Education, Geneva University Hospitals, Geneva, Switzerland.
Aims/hypothesis: Gestational diabetes mellitus (GDM) affects 14% of all pregnancies worldwide and is associated with cardiometabolic risk. We aimed to exploit high-resolution wearable device time-series data to create a fine-grained physiological characterisation of the postpartum GDM state in free-living conditions, including clinical variables, daily glucose dynamics, food and drink consumption, physical activity, sleep patterns and heart rate.
Methods: In a prospective observational study, we employed continuous glucose monitors (CGMs), a smartphone food diary, triaxial accelerometers and heart rate and heart rate variability monitors over a 2 week period to compare women who had GDM in the previous pregnancy (GDM group) and women who had a pregnancy with normal glucose metabolism (non-GDM group) at 1-2 months after delivery (baseline) and 6 months later (follow-up).
Appl Clin Inform
October 2024
Quality and Safety, Boston Medical Center, Boston, United States.
Background Opioid overdoses have contributed significantly to mortality in the United States. Despite long-standing recommendations from the Centers for Disease Control and Prevention to co-prescribe naloxone for patients receiving opioids who are at high risk of overdose, compliance with these guidelines has remained low. Objectives The objective of this study was to develop and evaluate a hospital-wide electronic health record (EHR)-based clinical decision support (CDS) tool designed to promote naloxone co-prescription for high-risk opioids.
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