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Introduction Although neuroscience has made tremendous progress towards understanding the basic neural circuitry underlying important processes such as attention, memory and emotion, little progress has been made in applying these insights to psychiatric populations to make clinically meaningful treatment predictions. The overall aim of the Tulsa 1000 (T-1000) study is to use the NIMH Research Domain Criteria framework in order to establish a robust and reliable dimensional set of variables that quantifies the positive and negative valence, cognition and arousal domains, including interoception, to generate clinically useful treatment predictions. Methods and analysis The T-1000 is a naturalistic study that will recruit, assess and longitudinally follow 1000 participants, including healthy controls and treatment-seeking individuals with mood, anxiety, substance use and eating disorders. Each participant will undergo interview, behavioural, biomarker and neuroimaging assessments over the course of 1 year. The study goal is to determine how disorders of affect, substance use and eating behaviour organise across different levels of analysis (molecules, genes, cells, neural circuits, physiology, behaviour and self-report) to predict symptom severity, treatment outcome and long-term prognosis. The data will be used to generate computational models based on Bayesian statistics. The final end point of this multilevel latent variable analysis will be standardised assessments that can be developed into clinical tools to help clinicians predict outcomes and select the best intervention for each individual, thereby reducing the burden of mental disorders, and taking psychiatry a step closer towards personalised medicine. Ethics and dissemination Ethical approval was obtained from Western Institutional Review Board screening protocol #20101611. The dissemination plan includes informing health professionals of results for clinical practice, submitting results to journals for peer-reviewed publication, presenting results at national and international conferences and making the dataset available to researchers and mental health professionals. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To examine short-term changes in patients' clinical status following lumbar spine surgery (LSS) and to explore presurgical variables that predict surgical outcomes. METHODS: Prospective cohort study. A total of 46 patients underwent LSS. Patients completed the following questionnaires 1 week before LSS and 2 weeks after discharge from the hospital: back and leg visual pain analogue scale, Ronald Morris questionnaire (RMQ), Modified Somatic Perception questionnaire (MSPQ), SF-36, Fear- Avoidance Beliefs Questionnaire, Beck's Depression Inventory, EuroQol questionnaire, and patient-perception of improvement. Regression models were constructed to examine predictors of pain, function, quality of life, and patient-perception of improvement at 2 weeks postsurgery. RESULTS: Patients demonstrated significant improvement in back and leg pain and function. MSPQ and symptom duration were significant predictors of back pain, while type of diagnosis and use of opioids were significant predictors of leg pain. Preoperative MSPQ and RMQ were significant predictors of postoperative RMQ. MSPQ, gender, and back pain were significant predictors of quality of life. Back pain, leg pain, depression, smoking, and worker's compensation were significantly associated with patientperception of improvement. CONCLUSIONS: This preliminary study could be viewed as a directory to identify potential risk factors for unfavorable outcomes at early stages following LSS. [ABSTRACT FROM AUTHOR]

OBJECTIVE: T o explore factors associated with discharge placement (DP) and need for skilled assistance after patients are discharged from hospital following lumbar laminectomy. METHODS: A retrospective analysis of 339 patients who underwent lumbar laminectomy was conducted. We used multivariable logistic regression analysis to identify significant covariates and to construct two regression models: a primary model to predict DP, home vs inpatient rehabilitation/skilled nursing facility (IR/SNF), and a secondary model to predict the need for skilled assistance once patients are discharged to home. RESULTS: The sample included 48.7% females, 68.2% married, 56.3% independent in daily activities, and 85.2% discharged to home. Subjects were a mean 56.06±12.75 years old and had a BMI of 31.35+6.2. Of those discharged to home, 17.7% needed skilled assistance. Patients stayed 4.41+3.55 days in the hospital and walked 203.38+144.87 ft during hospital stay. Age, distance walked during hospital stay, and length of hospital stay (LOS) were significant positive predictors for discharge to home vs IR/SNF, whereas single living status, diminished prior level of function, and longer LOS were predictors of need for skilled assistance after discharge to home. CONCLUSION: Age, mobility, marital status, prior level of function, and LOS are key variables in determining healthcare needs following lumbar laminectomy. [ABSTRACT FROM AUTHOR]

Texts and software that we are currently using for teaching multivariate analysis to non-statisticians lack in the delivery of confirmatory factor analysis (CFA). The purpose of this paper is to provide educators with a complement to these resources that includes CFA and its computation. We focus on how to use CFA to estimate a "composite reliability" of a psychometric instrument. This paper provides step-by-step guidance for introducing, via a case-study, the non-statistician to CFA. As a complement to our instruction about the more traditional SPSS, we successfully piloted the software R for estimating CFA on nine non-statisticians. This approach can be used with healthcare graduate students taking a multivariate course, as well as modified for community stakeholders of our Center for American Indian Community Health (e.g. community advisory boards, summer interns, & research team members).The placement of CFA at the end of the class is strategic and gives us an opportunity to do some innovative teaching: (1) build ideas for understanding the case study using previous course work (such as ANOVA); (2) incorporate multi-dimensional scaling (that students already learned) into the selection of a factor structure (new concept); (3) use interactive data from the students (active learning); (4) review matrix algebra and its importance to psychometric evaluation; (5) show students how to do the calculation on their own; and (6) give students access to an actual recent research project. [ABSTRACT FROM AUTHOR]

The article discusses a study which explores the equivalent forms of the arithematic-geometric mean inequality in probability. The study found that the equivalent forms of the arithematic-geometric mean inequality in probability and mathematical statistics have not been linked together in a formal way. The study also assessed that the equivalent forms of the arithematic-geometric mean inequality is equivalent to other renowned inequalities, including Lyapunov and Minkowski, through the use of probabilistic arguments.