A Feedback and Evaluation System That Provokes Minimal Retaliation by Trainees.

BACKGROUND: Grade inflation is pervasive in educational settings in the United States. One driver of grade inflation may be faculty concern that assigning lower clinical performance scores to trainees will cause them to retaliate and assign lower teaching scores to the faculty member. The finding of near-zero retaliation would be important to faculty members who evaluate trainees. METHODS: The authors used a bidirectional confidential evaluation and feedback system to test the hypothesis that faculty members who assign lower clinical performance scores to residents subsequently receive lower clinical teaching scores. From September 1, 2008, to February 15, 2013, 177 faculty members evaluated 188 anesthesia residents (n = 27,561 evaluations), and 188 anesthesia residents evaluated 204 faculty members (n = 25,058 evaluations). The authors analyzed the relationship between clinical performance scores assigned by faculty members and the clinical teaching scores received using linear regression. The authors used complete dyads between faculty members and resident pairs to conduct a mixed effects model analysis. All analyses were repeated for three different epochs, each with different administrative attributes that might influence retaliation. RESULTS: There was no relationship between mean clinical performance scores assigned by faculty members and mean clinical teaching scores received in any epoch (P ≥ 0.45). Using only complete dyads, the authors' mixed effects model analysis demonstrated a very small retaliation effect in each epoch (effect sizes of 0.10, 0.06, and 0.12; P ≤ 0.01). CONCLUSIONS: These results imply that faculty members can provide confidential evaluations and written feedback to trainees with near-zero impact on their mean teaching scores.

Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome.

Identifying biomarkers for tuberculosis (TB) is an ongoing challenge in developing immunological correlates of infection outcome and protection. Biomarker discovery is also necessary for aiding design and testing of new treatments and vaccines. To effectively predict biomarkers for infection progression in any disease, including TB, large amounts of experimental data are required to reach statistical power and make accurate predictions. We took a two-pronged approach using both experimental and computational modeling to address this problem. We first collected 200 blood samples over a 2- year period from 28 non-human primates (NHP) infected with a low dose of Mycobacterium tuberculosis. We identified T cells and the cytokines that they were producing (single and multiple) from each sample along with monkey status and infection progression data. Machine learning techniques were used to interrogate the experimental NHP datasets without identifying any potential TB biomarker. In parallel, we used our extensive novel NHP datasets to build and calibrate a multi-organ computational model that combines what is occurring at the site of infection (e.g., lung) at a single granuloma scale with blood level readouts that can be tracked in monkeys and humans. We then generated a large in silico repository of in silico granulomas coupled to lymph node and blood dynamics and developed an in silico tool to scale granuloma level results to a full host scale to identify what best predicts Mycobacterium tuberculosis (Mtb) infection outcomes. The analysis of in silico blood measures identifies Mtb-specific frequencies of effector T cell phenotypes at various time points post infection as promising indicators of infection outcome. We emphasize that pairing wetlab and computational approaches holds great promise to accelerate TB biomarker discovery.

On the formation of Dodd-Frank Act derivatives regulations.

Following the 2007-2009 financial crisis, governments around the world passed laws that marked the beginning of new period of enhanced regulation of the financial industry. These laws called for a myriad of new regulations, which in the U.S. are created through the so-called notice-and-comment process. Through examining the text documents generated through this process, we study the formation of regulations to gain insight into how new regulatory regimes are implemented following major laws like the landmark Dodd-Frank Wall Street Reform and Consumer Protection Act. Due to the variety of constituent preferences and political pressures, we find evidence that the government implements rules strategically to extend the regulatory boundary by first pursuing procedural rules that establish how economic activities will be regulated, followed by specifying who is subject to the procedural requirements. Our findings together with the unique nature of the Dodd-Frank Act translate to a number of stylized facts that should guide development of formal models of the rule-making process.

Structural and functional discovery in dynamic networks with non-negative matrix factorization.

Time series of graphs are increasingly prevalent in modern data and pose unique challenges to visual exploration and pattern extraction. This paper describes the development and application of matrix factorizations for exploration and time-varying community detection in time-evolving graph sequences. The matrix factorization model allows the user to home in on and display interesting, underlying structure and its evolution over time. The methods are scalable to weighted networks with a large number of time points or nodes and can accommodate sudden changes to graph topology. Our techniques are demonstrated with several dynamic graph series from both synthetic and real-world data, including citation and trade networks. These examples illustrate how users can steer the techniques and combine them with existing methods to discover and display meaningful patterns in sizable graphs over many time points.