Age- and sex-related patterns of first fracture and fracture prevalence

There are few data documenting the pattern of prevalent fracture across the entire adult age range, so we aimed to address this gap by investigating the prevalence of fractures in an Australian cohort. All-cause (ever) fractures were identified for males and females enrolled in the Geelong Osteoporosis Study (Australia) using a combination of radiology-confirmed and self-reported data. First fractures were used to generate age-related frequencies of individuals who had ever sustained a fracture. Of 1,538 males and 1,731 females, 927 males and 856 females had sustained at least one fracture since birth. The proportion of all prevalent fractures in the 0-10 year age group was similar for both sexes (~10 %). In males, the proportion with prevalent fracture increased to 34.1 % for age 11-20 year. Smaller increases were observed into mid-life, reaching a plateau at ~50 % from mid to late life. The age-related prevalence of fracture for females showed a more gradual increase until mid-life. For adulthood prevalent fractures, approximately 20 % of males had sustained a first adulthood fracture in the 20-30 year age group, with a gradual increase up to the oldest age group (49.1 %), while females showed an exponential pattern of increase from the 20-30 year age group (6.8 %) to the oldest age group (60.4 %). In both sexes, those who had not sustained a fracture in childhood or early adulthood generally appeared to remain fracture-free until at least the sixth decade. When considering the prevalence of adulthood fractures across the age groups, males showed a gradual increase while females showed an exponential increase.

Model to predict inpatient mortality from information gathered at presentation to an emergency department: The Triage Information Mortality Model (TIMM)

OBJECTIVES: To derive and validate a mortality prediction model from information available at ED triage. METHODS: Multivariable logistic regression of variables from administrative datasets to predict inpatient mortality of patients admitted through an ED. Accuracy of the model was assessed using the receiver operating characteristic area under the curve (ROC-AUC) and calibration using the Hosmer-Lemeshow goodness of fit test. The model was derived, internally validated and externally validated. Derivation and internal validation were in a tertiary referral hospital and external validation was in an urban community hospital. RESULTS: The ROC-AUC for the derivation set was 0.859 (95% CI 0.856-0.865), for the internal validation set was 0.848 (95% CI 0.840-0.856) and for the external validation set was 0.837 (95% CI 0.823-0.851). Calibration assessed by the Hosmer-Lemeshow goodness of fit test was good. CONCLUSIONS: The model successfully predicts inpatient mortality from information available at the point of triage in the ED.

SUMLOW: Early design-stage sketching of UML diagrams on an e-whiteboard

Most visual diagramming tools provide point-and-click construction of computer-drawn diagram elements using a conventional desktop computer and mouse. SUMLOW is a unified modelling language (UML) diagramming tool that uses an electronic whiteboard (E-whiteboard) and sketching-based user interface to support collaborative software design. SUMLOW allows designers to sketch UML constructs, mixing different UML diagram elements, diagram annotations, and hand-drawn text. A key novelty of the tool is the preservation of hand-drawn diagrams and support for manipulation of these sketches using pen-based actions. Sketched diagrams can be automatically 'formalized' into computer-recognized and -drawn UML diagrams and then exported to a third party CASE tool for further extension and use. We describe the motivation for SUMLOW, illustrate the use of the tool to sketch various UML diagram types, describe its key architecture abstractions and implementation approaches, and report on two evaluations of the toolset. We hope that our experiences will be useful for others developing sketching-based design tools or those looking to leverage pen-based interfaces in software applications.

Development and interrater reliability of the UK Mental Health Triage Scale

Mental health triage scales are clinical tools used at point of entry to specialist mental health service to provide a systematic way of categorizing the urgency of clinical presentations, and determining an appropriate service response and an optimal timeframe for intervention. The aim of the present study was to test the interrater reliability of a mental health triage scale developed for use in UK mental health triage and crisis services. An interrater reliability study was undertaken. Triage clinicians from England and Wales (n = 66) used the UK Mental Health Triage Scale (UK MHTS) to rate the urgency of 21 validated mental health triage scenarios derived from real occasions of triage. Interrater reliability was calculated using Kendall's coefficient of concordance (w) and intraclass correlation coefficient (ICC) statistics. The average ICC was 0.997 (95% confidence interval (CI): 0.996-0.999 (F (20, 1300) = 394.762, P < 0.001). The single measure ICC was 0.856 (95% CI: 0.776-0.926 (F (20, 1300) = 394.762, P < 0.001). The overall Kendall's w was 0.88 (P < 0.001). The UK MHTS shows substantial levels of interrater reliability. Reliable mental health triage scales employed within effective mental health triage systems offer possibilities for not only improved patient outcomes and experiences, but also for efficient use of finite specialist mental health services.