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e., a tendency to use the rating scale in a certain systematic way that is unrelated to the content of the items) and overconfidence (i.e., the differences in predicted performance based on surveys' responses and a prior knowledge test). We found that the response style bias accounts for a modest to a substantial amount of variation in the outcomes of the several self-report instruments, as well as in the course performance data. It is only the trace data, notably that of process type, that stand out in being independent of these response style patterns. The effect of overconfidence bias is limited. Given that empirical models in education typically aim to explain the outcomes of learning processes or the relationships between antecedents of these learning outcomes, our analyses suggest that the bias present in surveys adds predictive power in the explanation of performance data and other questionnaire data.Traditionally, firm competition has been studied in contexts where the dimensionality of the product attribute space is given, and firms deploy their strategies constrained by this space. However, firms may exert influence on the local structure of the product attribute space by offering product variants with new attributes. As a result, the geometry of the product attribute space would change endogenously through firms' actions, and this emergent new geometry modifies the conditions for subsequent firm behavior. By focusing on this interplay between actors and conditions, we explore the co-evolution of the firm and the product attribute space. Through a multi-variant Cournot competition framework, we develop a computational model in which firms invest to differentiate their products from other variants, but as minimally as possible so that demand from closely similar existing variants can be stolen. We introduce the fraction dimensionality of the attribute space as our critical independent variable, to reflect saturation of the space with product varieties. The simulation reveals that while new product variants are typically introduced by firms with scale economies, their performance gap with firms without scale economies reduces as fraction dimensionality increases. This indicates that space geometry evolution may favor small-scale players, even when their large-scale competitors are the driving force behind attribute space changes.It is important to calculate the drug removal by hemodialysis (HD) for drug dosing regimens in HD patients. However, there are limited and inconsistent information about the dialyzability of drugs by HD. Therefore, the aim of our study is to evaluate drug removal by utilizing a rat model of HD (HD rat) and to extrapolate this result to the drug removal rate in HD patients. HD rats received bilateral nephrectomy and HD for 2 h. The dialysis removal of 6 drugs was evaluated in HD rats. Dialysis efficiency, plasma protein binding rate (PBR) and distribution volume (Vd) of drugs were also measured. Furthermore, we examined the correlation between the dialyzability of drug in HD rats and humans and constructed the prediction formula of the drug dialyzability in HD patients. The clearance of urea and creatinine and normalized dialysis dose in HD rats were 0.83 ± 0.07 mL/min, 0.70 ± 0.08 mL/min, and 0.13 ± 0.06, respectively. The drug dialyzability in HD rats was similar to reported clinical data except for doripenem. A higher correlation was observed between drug dialyzability in reported clinical data and HD rats which were adjusted for PBR (r2 = 0.936; p less then 0.001) compared to unadjusted (r2 = 0.812; p = 0.009). Therefore, we constructed the prediction formula of the drug dialyzability in HD patients by utilizing the HD rat model and PBR. This study is useful for evaluating the dialyzability of high-risk drugs in a clinical setting and might provide appropriate preclinical dialyzability data for new drug.Background Patient empowerment is a key factor in improving health outcomes. Objective To evaluate the psychometric properties of the Spanish version of the questionnaire on Patient Empowerment in Long-Term Conditions (PELC) that evaluates the degree of empowerment of patients with chronic diseases. Methods Three measurements were made (at baseline, 2 weeks and 12 weeks) of quality of life (QoL), self-care, self-efficacy and empowerment. Reliability was evaluated as internal consistency for the entire sample. selleck inhibitor Test-retest reproducibility was evaluated for patients who were stable from baseline to week 2 (n = 70). Validity was analysed (n = 124) as baseline correlations with QoL, self-care, self-efficacy, clinical data and psychosocial variables. Sensitivity to change was analysed in terms of effect size for patients who had improved between baseline and week 12 (n = 48). Results The study was carried out with 124 patients with a diagnosis of heart failure. Cronbach's alpha was high, at >0.9, and the interclass correlation coefficient was low, at 0.47. PELC questionnaire scores showed differences depending on New York Heart Association functional class (p less then 0.05) and, as posited in the a priori hypotheses, were moderately correlated with emotional dimensions of QoL (0.53) and self-efficacy (0.43). Effect size for the clinically improved subsample was moderate (0.67). Conclusions The results suggest that the Spanish version of the PELC questionnaire has appropriate psychometric properties in terms of internal consistency and validity and is low in terms of reproducibility and sensitivity to change.Urbanization fragments landscapes and can impede the movement of organisms through their environment, which can decrease population connectivity. Reduction in connectivity influences gene flow and allele frequencies, and can lead to a reduction in genetic diversity and the fixation of certain alleles, with potential negative effects for populations. Previous studies have detected effects of urbanization on genetic diversity and structure in terrestrial animals living in landscapes that vary in their degree of urbanization, even over very short distances. We investigated the effects of low-intensity urbanization on genetic diversity and genetic structure in Song Sparrows (Melospiza melodia). We captured 208 Song Sparrows at seven sites along a gradient of urbanization in and around Blacksburg, VA, USA, then genotyped them using a panel of fifteen polymorphic microsatellite loci. We found that genetic diversity was comparable among the seven study sites, and there was no evidence of genetic structuring among sites. These findings suggest that over a gradient of urbanization characterized by low density urban development, Song Sparrows likely exist in a single panmictic population.Background School-based injuries represent a sizeable portion of child injuries. This study investigated the rates of school-based injuries in Lebanon, examining injury mechanisms, outcomes and associated risk factors. Methods Data were prospectively collected by intern school nurses at 11 private schools for the 2018-2019 academic year. Descriptive and inferential analyses were performed. Chi-square comparisons were conducted to determine the significance of any differences in injury rates between boys and girls for each category of school. Results 4,619 injury cases were collected. The yearly rate for school injuries was 419.1 per 1,000 children for the year 2018-2019. Boys demonstrated a significantly higher injury rate for all mechanisms of injuries, with the exception of being injured while walking, injured in the gym/sports areas, and other areas outside the playground and classroom. Elementary school children had the highest rate of injuries, nearly 2.4 times higher than kindergarten, 2.8 times higher than middle school, and 14.5 times higher than high school. Injuries to the face, upper extremities, and lower extremities were nearly 3 times more common than injuries to other areas of the body. Bumps/hits and bruises were most common-almost 3 times more likely than all other injury types. Injuries were mainly minor or moderate in severity-severe injuries were about 10 times less likely. Most injuries were unintentional, with rates nearly 5 times higher than those with unclear intent and 12 times higher than intentional injuries. Conclusions School injuries represent a relatively common problem. Compliance with playground safety standards coupled with the implementation of injury prevention strategies and active supervision at schools can curtail child injuries and ensure a safe and injury-free school environment.Background Maternal continuums of care were vital to reducing maternal and neonatal mortalities. While the dropout rate remains high and limited studies were found on risk factors associated with a high dropout rate of the maternal continuum of care. Objective This study aimed to assess the magnitude of dropout rate and its associated factors of maternity continuum of care in Ethiopia, 2016. Methods An in-depth secondary data analysis was conducted from the Ethiopian Demographic and Health Survey 2016 data. A total of 4,693 women who were booked for antenatal care visit were included to the final analysis. A community-based cross-sectional study design and a pre-tested and standardized questionnaire were used to collect the survey data. Data were weighted using women data weighting variables. Chi-square and multicollinearity assumptions were checked for independent variables. Bi-variable and multivariable logistics regression used to identify associated factors with a cut of the p-value of 0.2 and 0.05 respecns Dropout of women from the maternity continuum of care after antenatal care booking was a public health problem in Ethiopia. Socio-demographic, pregnancy, and health service-related factors were significant determinants of dropout from the maternity continuum of care. Improving the family wealth index, increasing access to health facilities, media exposure, and giving more information during the antenatal care visit is important to reduce the dropout rate from the maternity continuum of care.Background and purpose Machine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke. Methods We searched PubMed and Web of Science from 1990 to March 2019, using previously published search filters for stroke, ML, and prediction models. We focused on structured clinical data, excluding image and text analysis. This review was registered with PROSPERO (CRD42019127154). Results Eighteen studies were eligible for inclusion. Most studies reported less than half of the terms in the reporting quality checklist. The most frequently predicted stroke outcomes were mortality (7 studies) and functional outcome (5 studies). The most commonly used ML methods were random forests (9 studies), support vector machines (8 studies), decision trees (6 studies), and neural networks (6 studies). The median sample size was 475 (range 70-3184), with a median of 22 predictors (range 4-152) considered. All studies evaluated discrimination with thirteen using area under the ROC curve whilst calibration was assessed in three. Two studies performed external validation. None described the final model sufficiently well to reproduce it. Conclusions The use of ML for predicting stroke outcomes is increasing. However, few met basic reporting standards for clinical prediction tools and none made their models available in a way which could be used or evaluated. Major improvements in ML study conduct and reporting are needed before it can meaningfully be considered for practice.