886 research outputs found

    Predictors of failed attendances in a multi-specialty outpatient centre using electronic databases.

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    BACKGROUND: Failure to keep outpatient medical appointments results in inefficiencies and costs. The objective of this study is to show the factors in an existing electronic database that affect failed appointments and to develop a predictive probability model to increase the effectiveness of interventions. METHODS: A retrospective study was conducted on outpatient clinic attendances at Tan Tock Seng Hospital, Singapore from 2000 to 2004. 22864 patients were randomly sampled for analysis. The outcome measure was failed outpatient appointments according to each patient's latest appointment. RESULTS: Failures comprised of 21% of all appointments and 39% when using the patients' latest appointment. Using odds ratios from the mutliple logistic regression analysis, age group (0.75 to 0.84 for groups above 40 years compared to below 20 years), race (1.48 for Malays, 1.61 for Indians compared to Chinese), days from scheduling to appointment (2.38 for more than 21 days compared to less than 7 days), previous failed appointments (1.79 for more than 60% failures and 4.38 for no previous appointments, compared with less than 20% failures), provision of cell phone number (0.10 for providing numbers compared to otherwise) and distance from hospital (1.14 for more than 14 km compared to less than 6 km) were significantly associated with failed appointments. The predicted probability model's diagnostic accuracy to predict failures is more than 80%. CONCLUSION: A few key variables have shown to adequately account for and predict failed appointments using existing electronic databases. These can be used to develop integrative technological solutions in the outpatient clinic

    Capsular profiling of the Cronobacter genus and the association of specific Cronobacter sakazakii and C. malonaticus capsule types with neonatal meningitis and necrotizing enterocolitis

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    Background: Cronobacter sakazakii and C. malonaticus can cause serious diseases especially in infants where they are associated with rare but fatal neonatal infections such as meningitis and necrotising enterocolitis. Methods: This study used 104 whole genome sequenced strains, covering all seven species in the genus, to analyse capsule associated clusters of genes involved in the biosynthesis of the O-antigen, colanic acid, bacterial cellulose, enterobacterial common antigen (ECA), and a previously uncharacterised K-antigen. Results: Phylogeny of the gnd and galF genes flanking the O-antigen region enabled the defining of 38 subgroups which are potential serotypes. Two variants of the colanic acid synthesis gene cluster (CA1 and CA2) were found which differed with the absence of galE in CA2. Cellulose (bcs genes) were present in all species, but were absent in C. sakazakii sequence type (ST) 13 and clonal complex (CC) 100 strains. The ECA locus was found in all strains. The K-antigen capsular polysaccharide Region 1 (kpsEDCS) and Region 3 (kpsMT) genes were found in all Cronobacter strains. The highly variable Region 2 genes were assigned to 2 homology groups (K1 and K2). C. sakazakii and C. malonaticus isolates with capsular type [K2:CA2:Cell+] were associated with neonatal meningitis and necrotizing enterocolitis. Other capsular types were less associated with clinical infections. Conclusion: This study proposes a new capsular typing scheme which identifies a possible important virulence trait associated with severe neonatal infections. The various capsular polysaccharide structures warrant further investigation as they could be relevant to macrophage survival, desiccation resistance, environmental survival, and biofilm formation in the hospital environment, including neonatal enteral feeding tubes

    The speciation and genotyping of Cronobacter isolates from hospitalised patients

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    The World Health Organization (WHO) has recognised all Cronobacter species as human pathogens. Among premature neonates and immunocompromised infants, these infections can be life-threatening, with clinical presentations of septicaemia, meningitis and necrotising enterocolitis. The neurological sequelae can be permanent and the mortality rate as high as 40 – 80 %. Despite the highlighted issues of neonatal infections, the majority of Cronobacter infections are in the elderly population suffering from serious underlying disease or malignancy and include wound and urinary tract infections, osteomyelitis, bacteraemia and septicaemia. However, no age profiling studies have speciated or genotyped the Cronobacter isolates. A clinical collection of 51 Cronobacter strains from two hospitals were speciated and genotyped using 7-loci multilocus sequence typing (MLST), rpoB gene sequence analysis, O-antigen typing and pulsed- field gel electrophoresis (PFGE). The isolates were predominated by C. sakazakii sequence type 4 (63 %, 32/51) and C. malonaticus sequence type 7 (33 %, 17/51). These had been isolated from throat and sputum samples of all age groups, as well as recal and faecal swabs. There was no apparent relatedness between the age of the patient and the Cronobacter species isolated. Despite the high clonality of Cronobacter , PFGE profiles differentiated strains across the sequence types into 15 pulsotypes. There was almost complete agreement between O-antigen typing and rpoB gene sequence analysis and MLST profiling. This study shows the value of applying MLST to bacterial population studies with strains from two patient cohorts, combined with PFGE for further discrimination of strains

    Locomotor and collision characteristics by phases of play during the 2017 rugby league World Cup

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    Understanding differences in locomotor and collision characteristics between phases of play can help rugby league coaches develop training prescription. There are no data currently available describing these differences at the elite international level. The aim of our study was to determine the differences in average speed (m∙min−1), high-speed running (>5.5 m∙s−1) per minute and collision frequencies per minute (n∙min−1) between attack and defence during the 2017 Rugby League World Cup (RLWC). Methods: Microtechnology data were collected from 24 male professional rugby league players from the same international squad across six matches of the RLWC. Data were then subject to exclusion criteria and stratified into forwards (n = 9) and backs (n = 7) before being analysed with linear mixed-effects models. Results: When comparing attack with defence, forwards and backs had substantially slower average speeds (effect size [ES]; ±90% confidence limits: −2.31; ±0.31 and −1.17; ±0.25) and substantially greater high-speed distance per minute (1.61; ±0.59 and 4.41; ±1.19). Forwards completed substantially more collisions per minute when defending (2.75; ±0.32) whilst backs completed substantially more when attacking (0.63; ±0.70). There was greater within- and between-player variability for collision frequency (coefficient of variation [CV] range; 25–28%) and high-speed distance (18–33%) per minute when compared to average speed (6–12%). Conclusions: There are distinct differences in locomotor and collision characteristics when attacking and defending during international rugby league match-play, yet the variability of high-speed running and collisions per minute is large. These data may be useful to plan or evaluate training practices

    Towards causal benchmarking of bias in face analysis algorithms

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    Measuring algorithmic bias is crucial both to assess algorithmic fairness, and to guide the improvement of algorithms. Current methods to measure algorithmic bias in computer vision, which are based on observational datasets, are inadequate for this task because they conflate algorithmic bias with dataset bias. To address this problem we develop an experimental method for measuring algorithmic bias of face analysis algorithms, which manipulates directly the attributes of interest, e.g., gender and skin tone, in order to reveal causal links between attribute variation and performance change. Our proposed method is based on generating synthetic ``transects'' of matched sample images that are designed to differ along specific attributes while leaving other attributes constant. A crucial aspect of our approach is relying on the perception of human observers, both to guide manipulations, and to measure algorithmic bias. Besides allowing the measurement of algorithmic bias, synthetic transects have other advantages with respect to observational datasets: they sample attributes more evenly allowing for more straightforward bias analysis on minority and intersectional groups, they enable prediction of bias in new scenarios, they greatly reduce ethical and legal challenges, and they are economical and fast to obtain, helping make bias testing affordable and widely available. We validate our method by comparing it to a study that employs the traditional observational method for analyzing bias in gender classification algorithms. The two methods reach different conclusions. While the observational method reports gender and skin color biases, the experimental method reveals biases due to gender, hair length, age, and facial hair

    Tissue Microenvironments Define and Get Reinforced by Macrophage Phenotypes in Homeostasis or during Inflammation, Repair and Fibrosis

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    Current macrophage phenotype classifications are based on distinct in vitro culture conditions that do not adequately mirror complex tissue environments. In vivo monocyte progenitors populate all tissues for immune surveillance which supports the maintenance of homeostasis as well as regaining homeostasis after injury. Here we propose to classify macrophage phenotypes according to prototypical tissue environments, e.g. as they occur during homeostasis as well as during the different phases of (dermal) wound healing. In tissue necrosis and/or infection, damage- and/or pathogen-associated molecular patterns induce proinflammatory macrophages by Toll-like receptors or inflammasomes. Such classically activated macrophages contribute to further tissue inflammation and damage. Apoptotic cells and antiinflammatory cytokines dominate in postinflammatory tissues which induce macrophages to produce more antiinflammatory mediators. Similarly, tumor-associated macrophages also confer immunosuppression in tumor stroma. Insufficient parenchymal healing despite abundant growth factors pushes macrophages to gain a profibrotic phenotype and promote fibrocyte recruitment which both enforce tissue scarring. Ischemic scars are largely devoid of cytokines and growth factors so that fibrolytic macrophages that predominantly secrete proteases digest the excess extracellular matrix. Together, macrophages stabilize their surrounding tissue microenvironments by adapting different phenotypes as feed-forward mechanisms to maintain tissue homeostasis or regain it following injury. Furthermore, macrophage heterogeneity in healthy or injured tissues mirrors spatial and temporal differences in microenvironments during the various stages of tissue injury and repair. Copyright (C) 2012 S. Karger AG, Base

    Healthcare costs in women with metastatic breast cancer receiving chemotherapy as their principal treatment modality

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    <p>Abstract</p> <p>Background</p> <p>The economic costs of treating patients with metastatic breast cancer have been examined in several studies, but available estimates of economic burden are at least a decade old. In this study, we characterize healthcare utilization and costs in the US among women with metastatic breast cancer receiving chemotherapy as their principal treatment modality.</p> <p>Methods</p> <p>Using a large private health insurance claims database (2000-2006), we identified all women initiating chemotherapy for metastatic breast cancer with no evidence of receipt of concomitant or subsequent hormonal therapy, or receipt of trastuzumab at anytime. Healthcare utilization and costs (inpatient, outpatient, medication) were estimated on a cumulative basis from date of chemotherapy initiation ("index date") to date of disenrollment from the health plan or the end of the study period, whichever occurred first. Study measures were cumulated over time using the Kaplan-Meier Sample Average (KMSA) method; 95% CIs were generated using nonparametric bootstrapping. Findings also were examined among the subgroup of patients with uncensored data.</p> <p>Results</p> <p>The study population consisted of 1444 women; mean (SD) age was 59.1 (12.1) years. Over a mean follow-up of 532 days (range: 3 to 2412), study subjects averaged 1.7 hospital admissions, 10.7 inpatient days, and 83.6 physician office and hospital outpatient visits. Mean (95% CI) cumulative total healthcare costs were 128,556(128,556 (118,409, $137,644) per patient. Outpatient services accounted for 29% of total costs, followed by medication other than chemotherapy (26%), chemotherapy (25%), and inpatient care (20%).</p> <p>Conclusions</p> <p>Healthcare costs-especially in the outpatient setting--are substantial among women with metastatic breast cancer for whom treatment options other than chemotherapy are limited.</p
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