9.2%) received a prescription for antibiotics and analgesics, and 1636 (67.0%) received a prescription for local treatment. SARS-COV-2 pandemic led to changes in the characteristics of dental emergency patients. Trauma, acute pulpitis, and acute periodontitis are the leading reasons patients refer to dental emergency centers. Dental emergency centers should optimize treatment procedures, optimize the staff, and reasonably allocate materials according to the changes to improve the on-site treatment capacity and provide adequate dental emergency care.SARS-COV-2 pandemic led to changes in the characteristics of dental emergency patients. Trauma, acute pulpitis, and acute periodontitis are the leading reasons patients refer to dental emergency centers. Dental emergency centers should optimize treatment procedures, optimize the staff, and reasonably allocate materials according to the changes to improve the on-site treatment capacity and provide adequate dental emergency care. Many patients with atrial fibrillation (AF) remain undiagnosed despite availability of interventions to reduce stroke risk. Predictive models to date are limited by data requirements and theoretical usage. We aimed to develop a model for predicting the 2-year probability of AF diagnosis and implement it as proof-of-concept (POC) in a production electronic health record (EHR). We used a nested case-control design using data from the Indiana Network for Patient Care. The development cohort came from 2016 to 2017 (outcome period) and 2014 to 2015 (baseline). A separate validation cohort used outcome and baseline periods shifted 2years before respective development cohort times. Machine learning approaches were used to build predictive model. Patients ≥ 18years, later restricted to age ≥ 40years, with at least two encounters and no AF during baseline, were included. In the 6-week EHR prospective pilot, the model was silently implemented in the production system at a large safety-net urban hospital. Three new diagnosed AF. Successful POC implementation of the model in an EHR provided a practical strategy to identify patients who may benefit from interventions to reduce their stroke risk.Using variables commonly available in the EHR, we created a predictive model to identify 2-year risk of developing AF in those previously without diagnosed AF. Successful POC implementation of the model in an EHR provided a practical strategy to identify patients who may benefit from interventions to reduce their stroke risk. Linear mixed models (LMM) are a common approach to analyzing data from cluster randomized trials (CRTs). Inference on parameters can be performed via Wald tests or likelihood ratio tests (LRT), but both approaches may give incorrect Type I error rates in common finite sample settings. The impact of different combinations of cluster size, number of clusters, intraclass correlation coefficient (ICC), and analysis approach on Type I error rates has not been well studied. G Protein antagonist Reviews of published CRTs find that small sample sizes are not uncommon, so the performance of different inferential approaches in these settings can guide data analysts to the best choices. Using a random-intercept LMM stucture, we use simulations to study Type I error rates with the LRT and Wald test with different degrees of freedom (DF) choices across different combinations of cluster size, number of clusters, and ICC. Our simulations show that the LRT can be anti-conservative when the ICC is large and the number of clusters is small, with the effect most pronouced when the cluster size is relatively large. Wald tests with the between-within DF method or the Satterthwaite DF approximation maintain Type I error control at the stated level, though they are conservative when the number of clusters, the cluster size, and the ICC are small. Depending on the structure of the CRT, analysts should choose a hypothesis testing approach that will maintain the appropriate Type I error rate for their data. Wald tests with the Satterthwaite DF approximation work well in many circumstances, but in other cases the LRT may have Type I error rates closer to the nominal level.Depending on the structure of the CRT, analysts should choose a hypothesis testing approach that will maintain the appropriate Type I error rate for their data. Wald tests with the Satterthwaite DF approximation work well in many circumstances, but in other cases the LRT may have Type I error rates closer to the nominal level.To date, healthcare ethics committees (HEC) have been the only ethics consultation model in the hospital setting in Spain, though their usefulness for ethical conflict resolution in daily practice has been questioned. Individual clinical ethics consultation (CEC) is a complementary ethics consultation model, which has proved efficacious in real-time ethical problem-solving. Although CEC is widely used in North America, its implementation in Europe is still marginal. In this document we present the general characteristics of CEC services, comparing their potential advantages and risks to those of HECs. We will then share relevant European experiences in CEC, as well as review the few CEC initiatives in Spain. Finally, we will share our recent CEC implementation strategy in a national, medium-sized, teaching hospital. We will summarise the minimum requirements that such a CEC service must meet in order to carry out its consulting activity organisational flexibility, well-trained professionals, with sufficient clinical experience, economical support, and organisational dependency on HECs.One of the keys to overcoming the COVID-19 pandemic is the development of the vaccine in order to immunize the population. In addition to the medical complications to obtain the vaccine, we highlight the presence of other problems, such as the dissemination of fake news that add difficulties to overcoming the global problem, especially due to its incidence in the field of anti-vaccine movements, which have developed, with special presence in Italy in recent years. For this, we warn of the need to be prepared to overcome the two pandemics that are developing in parallel, the one caused by the virus and the one generated by the fake news.