Curricular Reform in Two Medical School Tracks and the Impact on USMLE Scores

Purpose: The University of Arizona College of Medicine underwent several curricular revisions that began in 2006. These changes included (1) moving from a traditional to a systems-based curriculum, (2) adding a second campus location, and (3) altering the duration of clinical clerkships. We examined whether these curricular revisions impacted student performance on the United States Medical Licensing Examination (USMLE) step 1 and step 2 Clinical Knowledge (CK) examinations. Method: We examined curricular changes that took place from academic years 20062010 (classes of 20102014) compared to the previous traditional-based curriculum in two different medical school tracks under one university system. Academic years 20022005 served as control, and ten different curricular groups were examined. An ANOVA was conducted for each step exam, and all pairwise differences were examined using Tukeys honest significant differences. Statistical significance was established at p<0.05. Results: The first year of the revised preclerkship curriculum resulted in lower step 1 scores compared to the previously traditional curriculum. However, statistically significant mean increases in step 1 and 2 scores were found for curricular groups that experienced the revised preclerkship curriculum, a return to six-week clerkship rotations, and had completed all 4years at one specific campus on one specific medical track. Conclusion: With the integration of basic and clinical sciences in the first 2years and modifications to the clerkship rotations, the content of the curriculum was taught with more regard to what will ultimately benefit the practicing physician. This curricular reform led to higher scores particularly on the step 2 USMLE exam.his study was funded in part with a grant from the Department of Academic Affairs, University of Arizona College of Medicine – Phoenix

A unifying approach for food webs, phylogeny, social networks, and statistics

A food web consists of nodes, each consisting of one or more species. The role of each node as predator or prey determines the trophic relations that weave the web. Much effort in trophic food web research is given to understand the connectivity structure, or the nature and degree of dependence among nodes. Social network analysis (SNA) techniques—quantitative methods commonly used in the social sciences to understand network relational structure—have been used for this purpose, although postanalysis effort or biological theory is still required to determine what natural factors contribute to the feeding behavior. Thus, a conventional SNA alone provides limited insight into trophic structure. Here we show that by using novel statistical modeling methodologies to express network links as the random response of within- and internode characteristics (predictors), we gain a much deeper understanding of food web structure and its contributing factors through a unified statistical SNA. We do so for eight empirical food webs: Phylogeny is shown to have nontrivial influence on trophic relations in many webs, and for each web trophic clustering based on feeding activity and on feeding preference can differ substantially. These and other conclusions about network features are purely empirical, based entirely on observed network attributes while accounting for biological information built directly into the model. Thus, statistical SNA techniques, through statistical inference for feeding activity and preference, provide an alternative perspective of trophic clustering to yield comprehensive insight into food web structure

Analysis of survey on menstrual disorder among teenagers using Gaussian copula model with graphical lasso prior.

A high prevalence of menstrual disturbance has been reported among teenage girls, and research shows that there are delays in diagnosis of endometriosis among young girls. Using data from the Menstrual Disorder of Teenagers Survey (administered in 2005 and 2016), we propose a Gaussian copula model with graphical lasso prior to identify cohort differences in menstrual characteristics and to predict endometriosis. The model includes random effects to account for clustering by school, and we use the extended rank likelihood copula model to handle variables of mixed-type. The graphical lasso prior shrinks the elements in the precision matrix of a Gaussian distribution to encourage a sparse graphical structure, where the level of shrinkage is adaptable based on the strength of the conditional associations among questions in the survey. Applying our proposed model to the menstrual disorder data set, we found that menstrual disturbance was more pronouncedly reported over a decade, and we found some empirical differences between those girls with higher risk of developing endometriosis and the general population

The use of metformin is associated with decreased lumbar radiculopathy pain

Amber Taylor,1 Anton H Westveld,2,6 Magdalena Szkudlinska,1 Prathima Guruguri,1 Emil Annabi,3 Amol Patwardhan,3 Theodore J Price,4 Hussein N Yassine51Department of Medicine, University of Arizona, Tucson, AZ, USA; 2Statistics Laboratory, Bio5 Institute, Statistics GIDP, University of Arizona, Tucson, AZ, USA; 3Department of Anesthesia, University of Arizona, Tucson, AZ, USA; 4Department of Pharmacology, University of Arizona, Tucson, AZ, USA; 5Department of Medicine, University of Southern California, LA, CA, USA; 6Faculty of ESTeM, University of Canberra, Canberra, ACT, AustraliaAbstract: Lumbar radiculopathy pain represents a major public health problem, with few effective long-term treatments. Preclinical neuropathic and postsurgical pain studies implicate the kinase adenosine monophosphate activated kinase (AMPK) as a potential pharmacological target for the treatment of chronic pain conditions. Metformin, which acts via AMPK, is a safe and clinically available drug used in the treatment of diabetes. Despite the strong preclinical rationale, the utility of metformin as a potential pain therapeutic has not yet been studied in humans. Our objective was to assess whether metformin is associated with decreased lumbar radiculopathy pain, in a retrospective chart review. We completed a retrospective chart review of patients who sought care from a university pain specialist for lumbar radiculopathy between 2008 and 2011. Patients on metformin at the time of visit to a university pain specialist were compared with patients who were not on metformin. We compared the pain outcomes in 46 patients on metformin and 94 patients not taking metformin therapy. The major finding was that metformin use was associated with a decrease in the mean of &ldquo;pain now,&rdquo; by &minus;1.85 (confidence interval: &minus;3.6 to &minus;0.08) on a 0&ndash;10 visual analog scale, using a matched propensity scoring analysis and confirmed using a Bayesian analysis, with a significant mean decrease of &minus;1.36 (credible interval: &minus;2.6 to &minus;0.03). Additionally, patients on metformin showed a non-statistically significant trend toward decreased pain on a variety of other pain descriptors. Our proof-of-concept findings suggest that metformin use is associated with a decrease in lumbar radiculopathy pain, providing a rational for larger retrospective trials in different pain populations and for prospective trials, to test the effectiveness of metformin in reducing neuropathic pain.Keywords: neuropathy, mTOR, adenosine monophosphate activated kinase, diabete

Dynamic Stochastic Blockmodels: Statistical Models for Time-Evolving Networks

Abstract. Significant efforts have gone into the development of statistical models for analyzing data in the form of networks, such as social networks. Most existing work has focused on modeling static networks, which represent either a single time snapshot or an aggregate view over time. There has been recent interest in statistical modeling of dynamic networks, which are observed at multiple points in time and offer a richer representation of many complex phenomena. In this paper, we propose a state-space model for dynamic networks that extends the well-known stochastic blockmodel for static networks to the dynamic setting. We then propose a procedure to fit the model using a modification of the extended Kalman filter augmented with a local search. We apply the procedure to analyze a dynamic social network of email communication