Search

Brian D Nicholson,1 Paul Aveyard,1 Willie Hamilton,2 Clare R Bankhead,1 Constantinos Koshiaris,1 Sarah Stevens,1 Frederick DR Hobbs,1 Rafael Perera1 1Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX26GG, UK; 2College of Medicine and Health, University of Exeter, Exeter EX1 2LU, UK Purpose: To use recorded weight values to internally validate weight status and weight change coding in the primary care Electronic Health Record (EHR). Patients and methods: We included adult patients with weight-related Read codes recorded in the UK’s Clinical Practice Research Datalink EHR between 2000 and 2017. Weight status codes were compared to weight values recorded on the same day and positive predictive values (PPVs) were calculated for commonly used codes. Weight change codes were validated using three methods: the percentage (%) difference in kilograms at the time of the code and 1) the previous weight measurement, 2) the weight predicted using linear regression, and 3) the historic mean weight. Weight change codes were validated if estimates were consistent across two out of three methods. Results: A total of 8,108,481 weight codes were recorded in 1,000,002 patients’ EHR. Twice as many were recorded in females (n=5,208,593, 64%). The mean body mass index for “overweight” codes ranged from 31.9 kg/m2 to 46.9 kg/m2 and from 17.4 kg/m2 to 19.2 kg/m2 for “underweight” codes. PPVs for the most commonly used weight status codes ranged from 81.3% (80%–82.5%) to 99.3% (99.2%–99.4%). Across the estimation methods, and using only validated weight change codes, mean weight loss ranged from – 5.2% (SD 5.8%) to –7.9% (SD 7.3%) and mean weight gain from 4.2 % (SD 5.5%) to 7.9 % (SD 8.2%). The previous and predicted weight methods were most consistent. Conclusion: We have developed an internationally applicable methodology to internally validate weight-related EHR coding by using available weight measurement data. We demonstrate the UK Read codes that can be confidently used to classify weight status and weight change in the absence of weight values. We provide the first evidence from primary care that a Read code for unexpected weight loss represents a mean loss of ≥ 5 % in a 6-month period, which was broadly consistent across age groups and gender. Keywords: validation studies, electronic health records, body weight, weight loss, weight gain, primary health care, data quality

Brain tumor is associated with poor prognosis. The treatment option is severely limited for a patient with brain tumor, despite great advances in understanding the etiology and molecular biology of brain tumors that have lead to breakthroughs in developing pharmaceutical strategies, and ongoing NCI/Pharma-sponsored clinical trials. We reviewed the literature on molecular targeted agents in preclinical and clinical studies in brain tumor for the past decade, and observed that the molecular targeting in brain tumors is complex. This is because no single gene or protein can be affected by single molecular agent, requiring the use of combination molecular therapy with cytotoxic agents. In this review, we briefly discuss the potential molecular targets, and the challenges of targeted brain tumor treatment. For example, glial tumors are associated with over-expression of calcium-dependent potassium (KCa) channels, and high grade glioma express specific KCa channel gene (gBK) splice variants, and mutant epidermal growth factor receptors (EGFRvIII). These specific genes are promising targets for molecular targeted treatment in brain tumors. In addition, drugs like Avastin and Gleevec target the molecular targets such as vascular endothelial cell growth factor receptor, platelet-derived growth factor receptors, and BRC-ABL/Akt. Recent discovery of non-coding RNA, specifically microRNAs could be used as potential targeted drugs. Finally, we discuss the role of anti-cancer drug delivery to brain tumors by breaching the blood-brain tumor barrier. This non-invasive strategy is particularly useful as novel molecules and humanized monoclonal antibodies that target receptor tyrosine kinase receptors are rapidly being developed.

Abstract non disponibile

Evolutionary algorithms are suitable to solve damage identification problems in a multiobjective context. However, the performance of these methods can deteriorate quickly with increasing noise intensities originating numerous uncertainties. In this paper, a statistic structural damage detection method formulated in a multiobjective context is proposed. The statistic analysis is implemented to take into account the uncertainties existing in the structural model and measured structural modal parameters. The presented method is verified by a number of simulated damage scenarios. The effects of noise and damage levels on damage detection are investigated.

Cognitive behavior therapy (CBT) is an evidence-based intervention in contemporary clinical health care services. CBT was developed as a psychotherapy to overcome the deficits of psychoanalysis and behaviorism by Aron T. Beck. However, after 63 years, CBT has transformed from its early framework. In comparison to other psychotherapies, cognitive behavior therapy has rapidly changed and developed as an evidence-based clinical intervention. Therefore, this study explored the evolution of cognitive behavior therapy by focusing on its origin, development, limitations, clinical application, third waves, and future directions. Early CBT therapeutic framework has developed as a cognitive model of depression and developed as an intervention to address the negative core belief of individuals and rationalize maladaptive behaviors, thoughts, and emotions. However, clinicians have experienced several limitations and scholars have recognized theoretical limitations in CBT. Later, scholars on CBT have overcome these deficits and developed disorder-specific CBT therapies. Then, clinicians empirically evaluate those therapeutic frameworks and transform CBT into an evidence-based clinical intervention. Further, CBT has strength in integrating third-wave therapies. In the future, CBT will therapeutically further transform with technology, integrating advanced theories of pathology, and clinical demand of the present health care services. Therefore, as a psychotherapeutic intervention, cognitive behavior therapy has transformed from its early framework, and at present, it has developed empirically. Further, in the future, it will transform into a more effective psychotherapeutic intervention. [ABSTRACT FROM AUTHOR]