Mcclainrahbek3463

RESULTS Of the 85,972 encounters with 465 physicians, 11% resulted in a corticosteroid. The median physician prescribing rate was 4.0% (range less then 1% to 81%). Corticosteroid receipt was associated with higher satisfaction versus receiving nothing (OR2.54; 95%CI2.25-2.87). Patients who received both an antibiotic and a corticosteroid reported the highest satisfaction (OR 3.91; 95%CI3.27-4.68). There was no correlation between individual physicians' corticosteroid and antibiotic prescribing rates. CONCLUSIONS Corticosteroid receipt was associated with patient satisfaction. Most physicians rarely prescribed corticosteroids, yet a small number prescribed them frequently. Identification of high prescribing physicians for educational interventions could reduce use of corticosteroids for acute respiratory tract infections. The authors had access to all data and were involved in all aspects of this Clinical Communication to the Editor's preparation. They have no conflicts of interest to disclose and received no financial support in the preparation of this manuscript. CONTEXT Patients who have suffered from persistent symptoms often undergo lumbar spinal surgery (LSS). Motor imagery should be added to postoperative home exercises to reduce patient complaints. OBJECTIVE The aim of this study was to compare the effects of home exercise plus motor imagery and only home exercise in patients undergoing LSS. DESIGN A randomized controlled study. SETTINGS This study was designed by researchers at Dokuz Eylul University. PARTICIPANTS Thirty-seven patients undergoing LSS were randomized to motor imagery group (n = 19) and control group (n = 18). MAIN OUTCOME MEASURES Pain was measured by Visual Analogue Scale, disability related to low back pain by Oswestry Disability Index, pain-related fear by Tampa Scale of Kinesiophobia, depression by Beck Depression Inventory, quality of life by World Health Organization Quality of Life Scale-Short Form (WHOQOL-BREF). All assessments were repeated in the preoperative period, three weeks after and six weeks after the surgery. ONO-7300243 INTERVENTIONS Motor imagery group underwent home exercise plus motor imagery program applied by voice recording. Control group underwent only home exercise program. Exercise program compliance was monitored by exercise diary and telephone calls once every week. RESULTS There was a significant improvement in pain at rest and during activity, disability, kinesiophobia, depression, physical health and psychological sub-parameters of WHOQOL-BREF between preoperative period, and the third week and sixth week in both groups (p  less then  0.05). When comparing groups for gain scores, there was a more significant improvement in pain during activity in motor imagery group (p  less then  0.05). Motor imagery should be addressed as an effective treatment after LSS. INTRODUCTION Disease-related malnutrition (DRM) is underdiagnosed and underreported despite its well-known association with a worse prognosis. The emergence of Big Data and the application of artificial intelligence in Medicine have revolutionized the way knowledge is generated. The aim of this study is to assess whether a Big Data tool could help us detect the amount of DRM in our hospital. METHODOLOGY This was a descriptive, retrospective study using the Savana Manager® tool, which allows for automatically analyzing and extracting the relevant clinical information contained in the free text of the electronic medical record. A search was performed using the term "malnutrition", comparing the characteristics of patients with DRM to the population of hospitalized patients between January 2012 and December 2017. RESULTS Among the 180,279 hospitalization records with a discharge report in that period, only 4,446 episodes (2.47%) included the diagnosis of malnutrition. The mean age of patients with DRM was 75 years (SD 16), as compared to 59 years (SD 25) for the overall population. There were no sex differences (51% male). In-hospital death occurred in 7.08% of patients with DRM and 2.98% in the overall group. Mean stay was longer in patients with DRM (8 vs. 5 days, P less then .0001) and there were no significant differences in the 72-hour readmission rate. The most common diagnoses associated with DRM were heart failure (35%), respiratory infection (23%), urinary infection (20%), and chronic kidney disease (15%). CONCLUSION Underdiagnosis of DRM remains a problem. Savana Manager® helps us to better understand the profile of these patients. L.U.In silico cancer models have demonstrated great potential as a tool to improve drug design, optimise the delivery of drugs to target sites in the host tissue and, hence, improve therapeutic efficacy and patient outcome. However, there are significant barriers to the successful translation of in silico technology from bench to bedside. More precisely, the specification of unknown model parameters, the necessity for models to adequately reflect in vivo conditions, and the limited amount of pertinent validation data to evaluate models' accuracy and assess their reliability, pose major obstacles in the path towards their clinical translation. This review aims to capture the state-of-the-art in in silico cancer modelling of vascularised solid tumour growth, and identify the important advances and barriers to success of these models in clinical oncology. Particular emphasis has been put on continuum-based models of cancer since they - amongst the class of mechanistic spatio-temporal modelling approaches - are well-established in simulating transport phenomena and the biomechanics of tissues, and have demonstrated potential for clinical translation. Three important avenues in in silico modelling are considered in this contribution first, since systemic therapy is a major cancer treatment approach, we start with an overview of the tumour perfusion and angiogenesis in silico models. Next, we present the state-of-the-art in silico work encompassing the delivery of chemotherapeutic agents to cancer nanomedicines through the bloodstream, and then review continuum-based modelling approaches that demonstrate great promise for successful clinical translation. We conclude with a discussion of what we view to be the key challenges and opportunities for in silico modelling in personalised and precision medicine.