Trauma and acute care surgeons report prescribing less opioids over time.

IntroductionConfronted with the opioid epidemic, surgeons must play a larger role to reduce risk of opioid abuse while managing acute pain. Having a better understanding of the beliefs and practices of trauma and acute care surgeons regarding discharge pain management may offer potential targets for interventions beyond fixed legal mandates.MethodsAn Institutional Review Board-approved electronic survey was sent to trauma and acute care surgeons who are members of the American Association for the Surgery of Trauma, and trauma and acute care surgeons and nurse practitioners at a Level 1 trauma center in February 2018. The survey included four case-based scenarios and questions about discharge prescription practices and beliefs.ResultsOf 66 respondents, most (88.1%) were at academic institutions. Mean number of opioid tablets prescribed was 20-30 (range 5-90), with the fewest tablets prescribed for elective laparoscopic cholecystectomy and the most for rib fractures. Few prescribed both opioid and non-opioid medications (22.4% to 31.4 %). Most would not change the number/strength of medications (69.2%), dose (53.9%), or number of tablets of opioids (83.1%) prescribed if patients used opioids regularly prior to their operation. The most common factors that made providers more likely to prescribe opioids were high inpatient opioid use (32.4%), history of opioid use/abuse (24.5%), and if the patient lives far from the hospital (12.9%). Most providers in practice >5 years reported a decrease in opioids (71.9%) prescribed at discharge.ConclusionTrauma and acute care surgeons and nurse practitioners reported decreasing the number/amount of opioids prescribed over time. Patients with high opioid use in the hospital, history of opioid use/abuse, or who live far from the provider may be prescribed more opioids at discharge.Level of evidenceLevel IV

Sister Justina Segale and the New Woman: Tradition and Change in the Progressive Era

M. Christine Anderson discusses the usefulness of Justina Segale’s journal as a tool to teach undergraduates about women’s changing roles in the early twentieth century. Examples from the journal are cited. Similarities and differences between Segale and the “new woman” are discussed. While women’s entrance into the professions of teaching, nursing, and social work is often held up as a new development of the Progressive era, Catholic women religious had long been trained for these occupations. In her social service and educational capacities, Segale illustrates the complexity of women’s roles in this era. Anderson contrasts Segale’s experience and perspective working among immigrants with those of secular women doing the same work, such as Jane Addams. Segale’s ethnographic writing is more personal than sociological, with narratives and anecdotes that provide a window into individual lives. Finally, the journal “challenges assumptions about poor immigrants, about women in general, and women religious in particular.

Some Guidelines For Using Nonparametric Methods For Modeling Data From Response Surface Designs

Traditional response surface methodology focuses on modeling responses using parametric models with designs chosen to balance cost with adequate estimation of parameters and prediction in the design space. Using nonparametric smoothing to approximate the response surface offers both opportunities as well as problems. This article explores some conditions under which these methods can be appropriately used to increase the flexibility of surfaces modeled. The Box and Draper (1987) printing ink study is considered to illustrate the methods

A More Efficient Way Of Obtaining A Unique Median Estimate For Circular Data

The procedure for computing the sample circular median occasionally leads to a non-unique estimate of the population circular median, since there can sometimes be two or more diameters that divide data equally and have the same circular mean deviation. A modification in the computation of the sample median is suggested, which not only eliminates this non-uniqueness problem, but is computationally easier and faster to work with than the existing alternative

Effect Of Position Of An Outlier On The Influence Curve Of The Measures Of Preferred Direction For Circular Data

Circular or angular data occur in many fields of applied statistics. A common problem of interest in circular data is estimating a preferred direction and its corresponding distribution. It is complicated by the wrap-around effect on the circle, which exists because there is no natural minimum or maximum. The usual statistics employed for linear data are inappropriate for directional data, as they do not account for its circular nature. The robustness of the three common choices for summarizing the preferred direction (the sample circular mean, sample circular median and a circular analog of the Hodges-Lehmann estimator) are evaluated via their influence functions

Dispersion Measures and Analysis for Factorial Directional Data with Replicates

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147059/1/rssc02643.pd

How to Host a Data Competition: Statistical Advice for Design and Analysis of a Data Competition

Data competitions rely on real-time leaderboards to rank competitor entries and stimulate algorithm improvement. While such competitions have become quite popular and prevalent, particularly in supervised learning formats, their implementations by the host are highly variable. Without careful planning, a supervised learning competition is vulnerable to overfitting, where the winning solutions are so closely tuned to the particular set of provided data that they cannot generalize to the underlying problem of interest to the host. This paper outlines some important considerations for strategically designing relevant and informative data sets to maximize the learning outcome from hosting a competition based on our experience. It also describes a post-competition analysis that enables robust and efficient assessment of the strengths and weaknesses of solutions from different competitors, as well as greater understanding of the regions of the input space that are well-solved. The post-competition analysis, which complements the leaderboard, uses exploratory data analysis and generalized linear models (GLMs). The GLMs not only expand the range of results we can explore, they also provide more detailed analysis of individual sub-questions including similarities and differences between algorithms across different types of scenarios, universally easy or hard regions of the input space, and different learning objectives. When coupled with a strategically planned data generation approach, the methods provide richer and more informative summaries to enhance the interpretation of results beyond just the rankings on the leaderboard. The methods are illustrated with a recently completed competition to evaluate algorithms capable of detecting, identifying, and locating radioactive materials in an urban environment.Comment: 36 page

Impact of a novel after school program: Smart Fit Girls

Individuals who are highly physically active are more likely to have a greater self-esteem, better body image, and increased physical activity self-efficacy. Currently, the average PE program provides less than 12% of the recommended daily amount of physical activity, with adolescent girls being the least active. The primary purpose of this research is to explore the efficacy of an after-school program, Smart Fit Girls (SFG), which aims to improve adolescent girls physical activity habits, self-esteem and body image. A secondary purpose is to examine how physical activity and mother/daughter relationships affect adolescent girls physical and emotional health. Girls attending Riverside Middle School in Pendleton, SC and their mother or female guardian were recruited for this study. The girls were 10-14 years old, in good academic standing, and were not involved in school athletics. To explore the impact of SFG all participants and their mothers will complete two rounds (pre/post) of questionnaires and focus groups. A control group of daughters and mothers at R.C. Edwards in Clemson, SC will participate in quantitative and qualitative data collection as well. Preliminary data demonstrate an 11% increase in self-esteem in mothers and statistically significant improvements in body image between pre and post measurements in girl participants

\u3ci\u3eI\u3c/i\u3e-optimal or \u3ci\u3eG\u3c/i\u3e-optimal: Do We Have to Choose?

When optimizing an experimental design for good prediction performance based on an assumed second order response surface model, it is common to focus on a single optimality criterion, either G-optimality, for best worst-case prediction precision, or I-optimality, for best average prediction precision. In this article, we illustrate how using particle swarm optimization to construct a Pareto front of non-dominated designs that balance these two criteria yields some highly desirable results. In most scenarios, there are designs that simultaneously perform well for both criteria. Seeing alternative designs that vary how they balance the performance of G- and I-efficiency provides experimenters with choices that allow selection of a better match for their study objectives. We provide an extensive repository of Pareto fronts with designs for 17 common experimental scenarios for 2 (design size N = 6 to 12), 3 (N = 10 to 16) and 4 (N = 15, 17, 20) experimental factors. These, when combined with a detailed strategy for how to efficiently analyze, assess, and select between alternatives, provide the reader with the tools to select the ideal design with a tailored balance between G- and I- optimality for their own experimental situations