A comparison of the effectiveness of the team-based learning readiness assessments completed at home to those completed in class.

PURPOSE: The readiness assurance process (RAP) of team-based learning (TBL) is an important element that ensures that students come prepared to learn. However, the RAP can use a significant amount of class time which could otherwise be used for application exercises. The authors administered the TBL-associated RAP in class or individual readiness assurance tests (iRATs) at home to compare medical student performance and learning preference for physiology content. METHODS: Using cross-over study design, the first year medical student TBL teams were divided into two groups. One group was administered iRATs and group readiness assurance tests (gRATs) consisting of physiology questions during scheduled class time. The other group was administered the same iRAT questions at home, and did not complete a gRAT. To compare effectiveness of the two administration methods, both groups completed the same 12-question physiology assessment during dedicated class time. Four weeks later, the entire process was repeated, with each group administered the RAP using the opposite method. RESULTS: The performance on the physiology assessment after at-home administration of the iRAT was equivalent to performance after traditional in-class administration of the RAP. In addition, a majority of students preferred the at-home method of administration and reported that the at-home method was more effective in helping them learn course content. CONCLUSION: The at-home administration of the iRAT proved effective. The at-home administration method is a promising alternative to conventional iRATs and gRATs with the goal of preserving valuable in-class time for TBL application exercises.

Drug-drug interaction studies of cardiovascular drugs involving P-glycoprotein, an efflux transporter, on the pharmacokinetics of edoxaban, an oral factor Xa inhibitor.

BACKGROUND: Edoxaban, an oral direct factor Xa inhibitor, is in development for thromboprophylaxis, including prevention of stroke and systemic embolism in patients with atrial fibrillation (AF). P-glycoprotein (P-gp), an efflux transporter, modulates absorption and excretion of xenobiotics. Edoxaban is a P-gp substrate, and several cardiovascular (CV) drugs have the potential to inhibit P-gp and increase drug exposure. OBJECTIVE: To assess the potential pharmacokinetic interactions of edoxaban and 6 cardiovascular drugs used in the management of AF and known P-gp substrates/inhibitors. METHODS: Drug-drug interaction studies with edoxaban and CV drugs with known P-gp substrate/inhibitor potential were conducted in healthy subjects. In 4 crossover, 2-period, 2-treatment studies, subjects received edoxaban 60 mg alone and coadministered with quinidine 300 mg (n = 42), verapamil 240 mg (n = 34), atorvastatin 80 mg (n = 32), or dronedarone 400 mg (n = 34). Additionally, edoxaban 60 mg alone and coadministered with amiodarone 400 mg (n = 30) or digoxin 0.25 mg (n = 48) was evaluated in a single-sequence study and 2-cohort study, respectively. RESULTS: Edoxaban exposure measured as area under the curve increased for concomitant administration of edoxaban with quinidine (76.7 %), verapamil (52.7 %), amiodarone (39.8 %), and dronedarone (84.5 %), and exposure measured as 24-h concentrations for quinidine (11.8 %), verapamil (29.1 %), and dronedarone (157.6 %) also increased. Administration of edoxaban with amiodarone decreased the 24-h concentration for edoxaban by 25.7 %. Concomitant administration with digoxin or atorvastatin had minimal effects on edoxaban exposure. CONCLUSION: Coadministration of the P-gp inhibitors quinidine, verapamil, and dronedarone increased edoxaban exposure. Modest/minimal effects were observed for amiodarone, atorvastatin, and digoxin.

A Randomized Phase II Crossover Study of Imatinib or Rituximab for Cutaneous Sclerosis after Hematopoietic Cell Transplantation.

PURPOSE: Cutaneous sclerosis occurs in 20% of patients with chronic graft-versus-host disease (GVHD) and can compromise mobility and quality of life. EXPERIMENTAL DESIGN: We conducted a prospective, multicenter, randomized, two-arm phase II crossover trial of imatinib (200 mg daily) or rituximab (375 mg/m(2) i.v. weekly × 4 doses, repeatable after 3 months) for treatment of cutaneous sclerosis diagnosed within 18 months (NCT01309997). The primary endpoint was significant clinical response (SCR) at 6 months, defined as quantitative improvement in skin sclerosis or joint range of motion. Treatment success was defined as SCR at 6 months without crossover, recurrent malignancy or death. Secondary endpoints included changes of B-cell profiles in blood (BAFF levels and cellular subsets), patient-reported outcomes, and histopathology between responders and nonresponders with each therapy. RESULTS: SCR was observed in 9 of 35 [26%; 95% confidence interval (CI); 13%-43%] participants randomized to imatinib and 10 of 37 (27%; 95% CI, 14%-44%) randomized to rituximab. Six (17%; 95% CI, 7%-34%) patients in the imatinib arm and 5 (14%; 95% CI, 5%-29%) in the rituximab arm had treatment success. Higher percentages of activated B cells (CD27(+)) were seen at enrollment in rituximab-treated patients who had treatment success (P = 0.01), but not in imatinib-treated patients. CONCLUSIONS: These results support the need for more effective therapies for cutaneous sclerosis and suggest that activated B cells define a subgroup of patients with cutaneous sclerosis who are more likely to respond to rituximab.

Cross-Domain Multitask Learning with Latent Probit Models

Learning multiple tasks across heterogeneous domains is a challenging problem since the feature space may not be the same for different tasks. We assume the data in multiple tasks are generated from a latent common domain via sparse domain transforms and propose a latent probit model (LPM) to jointly learn the domain transforms, and the shared probit classifier in the common domain. To learn meaningful task relatedness and avoid over-fitting in classification, we introduce sparsity in the domain transforms matrices, as well as in the common classifier. We derive theoretical bounds for the estimation error of the classifier in terms of the sparsity of domain transforms. An expectation-maximization algorithm is derived for learning the LPM. The effectiveness of the approach is demonstrated on several real datasets.

Characteristics of primary care office visits to nurse practitioners, physician assistants and physicians in United States Veterans Health Administration facilities, 2005 to 2010: a retrospective cross-sectional analysis.

UNLABELLED: BACKGROUND: Primary care, an essential determinant of health system equity, efficiency, and effectiveness, is threatened by inadequate supply and distribution of the provider workforce. The Veterans Health Administration (VHA) has been a frontrunner in the use of nurse practitioners (NPs) and physician assistants (PAs). Evaluation of the roles and impact of NPs and PAs in the VHA is critical to ensuring optimal care for veterans and may inform best practices for use of PAs and NPs in other settings around the world. The purpose of this study was to characterize the use of NPs and PAs in VHA primary care and to examine whether their patients and patient care activities were, on average, less medically complex than those of physicians. METHODS: This is a retrospective cross-sectional analysis of administrative data from VHA primary care encounters between 2005 and 2010. Patient and patient encounter characteristics were compared across provider types (PA, NP, and physician). RESULTS: NPs and PAs attend about 30% of all VHA primary care encounters. NPs, PAs, and physicians fill similar roles in VHA primary care, but patients of PAs and NPs are slightly less complex than those of physicians, and PAs attend a higher proportion of visits for the purpose of determining eligibility for benefits. CONCLUSIONS: This study demonstrates that a highly successful nationwide primary care system relies on NPs and PAs to provide over one quarter of primary care visits, and that these visits are similar to those of physicians with regard to patient and encounter characteristics. These findings can inform health workforce solutions to physician shortages in the USA and around the world. Future research should compare the quality and costs associated with various combinations of providers and allocations of patient care work, and should elucidate the approaches that maximize quality and efficiency.

Measurement of the tt̅W and tt̅Z Cross Sections using Proton-Proton Collisions at √s = 8 TeV with the ATLAS Detector

A measurement of the production cross sections of top quark pairs in association with a W or Z boson is presented. The measurement uses 20.3 fb−1 of data from proton-proton collisions at √s = 8 TeV collected by the ATLAS detector at the Large Hadron Collider. Four different final states are considered: two opposite-sign leptons, two same-sign leptons, three leptons, and four leptons. The t t̅ W and t t̅ Z cross sections are simultaneously extracted using a maximum likelihood fit over all the final states. The t t̅ Z cross section is measured to be 176+58−52 fb, corresponding to a signal significance of 4.2σ. The t t̅ W cross section is measured to be 369+100−91 fb, corresponding to a signal significance of 5.0σ. The results are consistent with next-to-leading-order calculations for the tt̅W and tt̅Z processes.

RNA Virus Evolution: a Cross-scale, Disease Dynamic Perspective

RNA viruses are an important cause of global morbidity and mortality. The rapid evolutionary rates of RNA virus pathogens, caused by high replication rates and error-prone polymerases, can make the pathogens difficult to control. RNA viruses can undergo immune escape within their hosts and develop resistance to the treatment and vaccines we design to fight them. Understanding the spread and evolution of RNA pathogens is essential for reducing human suffering. In this dissertation, I make use of the rapid evolutionary rate of viral pathogens to answer several questions about how RNA viruses spread and evolve. To address each of the questions, I link mathematical techniques for modeling viral population dynamics with phylogenetic and coalescent techniques for analyzing and modeling viral genetic sequences and evolution. The first project uses multi-scale mechanistic modeling to show that decreases in viral substitution rates over the course of an acute infection, combined with the timing of infectious hosts transmitting new infections to susceptible individuals, can account for discrepancies in viral substitution rates in different host populations. The second project combines coalescent models with within-host mathematical models to identify driving evolutionary forces in chronic hepatitis C virus infection. The third project compares the effects of intrinsic and extrinsic viral transmission rate variation on viral phylogenies.