A Review of the “Open” and “Closed” Circulatory Systems: New Terminology for Complex Invertebrate Circulatory Systems in Light of Current Findings

Invertebrate cardiovascular systems have historically been viewed as sluggish, poorly regulated, and “open”, where blood bathes the tissues directly as it moves through a system of ill-defined sinuses and/or lacunae without an endothelial boundary. When examining cardiovascular/circulatory morphology and physiology in a broader evolutionary context, one can question the very nature of the definition of a “closed” versus “open” circulatory system. Viewed in this context a number of invertebrates have evolved incomplete or even completely cell-lined vessels and or lacunae with a highly branched vasculature that allows for the production of significant driving pressures and flows to meet relatively high metabolic demands driven by active life styles. In light of our current understanding of invertebrate cardiovascular systems and their paralleled complexity to vertebrate systems, a number of long established paradigms must be questioned and new definitions presented to better align our understanding of the nature of “open” versus “closed” cardiovascular systems

UNLV-GWLA webcast pt. 2

An Outcomes Based Approach to Undergraduate Education — Jennifer Fabbi & Carl Reiber For over three years, the Libraries have been heavily involved in the creation of a proposal to reform undergraduate education at UNLV. Librarians are serving in key leadership roles as we transition from a discipline-based, turf-driven curriculum, to one that is based on a set of essential learning outcomes to be infused throughout students\u27 entire experience, in both the curricular and co-curricular arenas. This partnership will outline some of the qualities that position librarians for leadership roles in campus-wide educational initiatives — from developing undergraduate learning outcomes to rising above the credit-count fray. All Roads Lead to Faculty Development — Vicki Nozero & Christine Bergman Summary of three campus-wide faculty development collaborative initiatives in which the University Libraries has played a leading role: the UNLV Faculty Institute on Research Based Learning for High Impact Courses, the Hotel Faculty Institute on Course Design, and the Faculty Collaboratory. Curricular and Co-Curricular Collaborations — Anne Zald & Alicia Simon In addition to the collaborations discussed by preceding speakers, the Libraries is pursuing seemingly disparate collaborations that share a fundamental goal, e.g. engagement with student learning. Zald will outline several of these initiatives while Simon will provide a case study of the impact of these partnerships

A Machine Learning Algorithm to Identify Patients at Risk of Unplanned Subsequent Surgery After Intramedullary Nailing for Tibial Shaft Fractures

Objectives: In the SPRINT trial, 18% of patients with a tibial shaft fracture (TSF) treated with intramedullary nailing (IMN) had one or more unplanned subsequent surgical procedures. It is clinically relevant for surgeon and patient to anticipate unplanned secondary procedures, other than operations that can be readily expected such as reconstructive procedures for soft tissue defects. Therefore, the objective of this study was to develop a machine learning (ML) prediction model using the SPRINT data that can give individual patients and their care team an estimate of their particular probability of an unplanned second surgery. Methods: Patients from the SPRINT trial with unilateral TSFs were randomly divided into a training set (80%) and test set (20%). Five ML algorithms were trained in recognizing patterns associated with subsequent surgery in the training set based on a subset of variables identified by random forest algorithms. Performance of each ML algorithm was evaluated and compared based on (1) area under the ROC curve, (2) calibration slope and intercept, and (3) the Brier score. Results: Total data set comprised 1198 patients, of whom 214 patients (18%) underwent subsequent surgery. Seven variables were used to train ML algorithms: (1) Gustilo-Anderson classification, (2) Tscherne classification, (3) fracture location, (4) fracture gap, (5) polytrauma, (6) injury mechanism, and (7) OTA/AO classification. The best-performing ML algorithm had an area under the ROC curve, calibration slope, calibration intercept, and the Brier score of 0.766, 0.954, -0.002, and 0.120 in the training set and 0.773, 0.922, 0, and 0.119 in the test set, respectively. Conclusions: An ML algorithm was developed to predict the probability of subsequent surgery after IMN for TSFs. This ML algorithm may assist surgeons to inform patients about the probability of subsequent surgery and might help to identify patients who need a different perioperative plan or a more intensive approach.Orthopaedics, Trauma Surgery and Rehabilitatio