Yoxas/statistical_literacy huggingface dataset:
Dolutegravir (DTG) based antiretroviral therapy (ART) has largely replaced Efavirenz (EFV) based therapy as the preferred first-line regimen in the treatment of adults with HIV. This study was carried out to evaluate the comparative cost-effectiveness of DTG and EFV-based ART in HIV-infected treatment-naïve patients in a treatment centre in Nigeria. This was a retrospective case-control study of patients initiated on DTG vs. EFV-based regimens from January 2018 to December 2019 at the APIN/HAVARD clinic of Nigeria's Jos University Teaching Hospital. The current viral load result was used to determine treatment effectiveness using a benchmark of ≤200 copies/mL. Sensitivity analysis was carried out to ensure the robustness of the benchmark. The total cost of treatment was obtained by summing up the relevant cost components. Appropriate descriptive and inferential statistics were employed in data analysis using Statistical Product and Services Solutions (SPSS) V.25. The incremental cost-effectiveness ratio of DTG compared to EFV was presented as cost/effectiveness. Treatment was effective in 42(51.9%) and 58(71.6%) patients initiated on DTG and EFV-based regimen, respectively. The incremental cost-effective ratio (ICER) of patients on DTG compared to those on EFV was $10.5076 per effectiveness, which was less than 1% of the Nigerian 2019 per capita Gross Domestic Product. Sensitivity analysis showed the robustness of the result. Efavirenz based regimen had higher treatment effectiveness than DTG-based regimen in treatment-naive patients after initiating treatment in a short term. Compared to EFV, DTG-based regimen is cost-effective in the management of treatment naïve HIV patients.
Domain-general skills such as executive functions (EFs), and domain-specific skills such as non-symbolic number sense and symbolic understanding are often pitted against each other as predictors of emerging maths. Here we aimed to investigate early childhood relations between these foundational skills with a balanced, longitudinal design. One hundred and seventy 3- and 4-year-old-children were tested at two time points, 5 months apart, on four domain-general executive and five domain-specific numeracy tasks. A latent EF factor was a strong predictor of symbolic maths and of their growth. In addition, stronger symbolic maths at Time 1 was correlated with later stronger EF, but symbolic maths did not predict EF growth. Our findings provide novel insights into dynamic interplay between general and specific cognitive skills contributing to preschool maths.
Domestic violence is violation of basic human rights, and poses a threat to the physical, mental, and social health aspects of women and her children. The causation of domestic violence is precipitated by many risk factors. Aim was to estimate the magnitude of domestic violence overall, and its sub-types, among pregnant women. The study also aimed to find the associated socio-demographic determinants of domestic violence among the subjects. The study was designed as a cross-sectional study to estimate the prevalence of domestic violence and to find socio-demographic correlates in its causation. A total of 1500 apparently healthy pregnant women, with gestation up to 20 weeks were interviewed. A pre-tested semi-structured interview schedule was used to collect data on socio-demographic variables and details of domestic violence, after explaining the purpose of study to the subjects and obtaining informed consent. Data was entered in Ms-Excel and IBM SPSS Version 25 was used for statistical analysis. The findings revealed overall prevalence of domestic violence to be 29.7%, with emotional and verbal type of violence being most common type. Caste, religion, literacy status of study subjects, and occupational status of spouses of study subjects were reported as significant correlates affecting the causation of domestic violence among the subjects. The findings highlight the burden and thus, stress for the need for effective involvement of all sectors in the elimination of domestic violence against women as a public health issue and develop zero-tolerance towards it.
Dravet syndrome is a rare, early childhood-onset epileptic and developmental encephalopathy. Responses to placebo in clinical trials for epilepsy therapies range widely, but factors influencing placebo response remain poorly understood. This study explored placebo response and its effects on safety, efficacy, and quality of life outcomes in patients with Dravet syndrome. We performed exploratory post-hoc analyses of pooled data from placebo-treated patients from the GWPCARE 1B and GWPCARE 2 randomized controlled phase III trials, comparing cannabidiol and matched placebo in 2-18 year old Dravet syndrome patients. All patients had ≥4 convulsive seizures during a baseline period of 4 weeks. 124 Dravet syndrome-treated patients were included in the analysis (2-5 years: n = 35; 6-12 years: n = 52; 13-18 years: n = 37). Convulsive seizures were experienced by all placebo group patients at all timepoints, with decreased median convulsive seizure frequency during the treatment period versus baseline; the number of convulsive seizure-free days was similar to baseline. Convulsive seizure frequency had a nominally significant positive correlation with age and a nominally significant negative correlation with body mass index. Most placebo-treated patients experienced a treatment-emergent adverse event; however, most resolved quickly, and serious adverse events were infrequent. Placebo treatment had very little effect on reported Caregiver Global Impression of Change outcomes versus baseline. Placebo had little impact on convulsive seizure-free days and Caregiver Global Impression of Change versus baseline, suggesting that these metrics may help differentiate placebo and active treatment effects in future studies. However, future research should further assess placebo responses to confirm these results.
Drawing connections between principles and worked examples is an approach to learning and instruction, but it is poorly understood. This study investigated the effects of principle and example complexity on learners' ability to map principles and worked examples. The complexity of a principle or example was determined based on the number of concepts and relationships involved. 138 college students were randomly assigned to one of the mapping conditions: principle-simple example, principle-complex example, simple example-simple example, simple example-complex example, and complex example-complex example. The participants studied related materials and completed a free-mapping and a guided-mapping task for a simple and a complex probability principle. The effects of the mapping activities were measured in terms of gains in structural and conceptual knowledge. For the simple principle, principle-example mapping led to fewer nonrelational comparisons (standalone concepts) than did example-example mapping and an equal number of relational comparisons (interconnected concepts). For the complex principle, principle-example mapping led to fewer nonrelational but more relational comparisons than example-example mapping did. Principle-example mapping of corresponding content was more difficult than example-example mapping was. However, principle-example mapping of noncorresponding content was as easy as or easier than example-example mapping. The two forms of mapping resulted in equivalent gains in structural and conceptual knowledge. The findings of this study expand the understanding of analogical reasoning and learning through mapping and comparison of abstract and concrete content. The findings indicate that principle-example mapping enables learners to overcome the obstacles of comprehending abstract or general information and to identify the interrelationships of the individual concepts in formal structures.
Drawing upon the framework of life course epidemiology, this study aligns with research on the mental health consequences of significant social transitions during early adulthood. The focus is on the variation in initial labour market attachment and the development of depressiveness, assuming that a firm attachment is associated with decreasing depressiveness. The baseline investigation of the studied cohort (n = 1,001) took place during their final year of compulsory schooling at age 16. Follow-up surveys were conducted at ages 18, 21, 30, and 43. Depressiveness was measured with a five-item score. Multiple trajectory analysis, incorporating five labour market statuses observed over seven half-year periods from ages 18 to 21, was employed to categorize the cohort into six distinct groups. Among these, 'All-time education,' 'From education to employment,' 'Education and employment,' and 'From employment to education' were considered to demonstrate firm labour market attachment. Meanwhile, 'Active labour market policy' and 'Unemployment' represented less firm attachment. The trajectory of depressive symptoms among the total cohort from age 16 to age 43 exhibited a 'broken stick' pattern, reaching its lowest point at age 21. This pattern was evident in all groups classified as having a firm attachment. A substantial decrease in depressiveness was also observed in the relatively weakly attached 'Active labour market policy' group, whereas no 'broken stick' pattern emerged in the 'Unemployment' group. The disparities in the levels of depressiveness observed at age 21 remained relatively stable across the measurements at ages 30 and 43. The results were as expected, except for the observed improvement in mental health within the 'Active labour market policy' group. Supported labour market attachment during emerging adulthood can enhance mental well-being similarly to regular mainstream attachment. In terms of policy recommendations, the consistently high levels of depressiveness within the 'Unemployment' group underscore the importance of reducing long-term and repeated unemployment in young age. The findings regarding the 'Active labour market policy' provide evidence of the intervention's benefits. While the primary goal of these measures is to create jobs for the unemployed, they also include elements that contribute to participants' mental health.
Drawings have been extensively used as a research method to gather data from research participants including school students regarding their perceptions of mathematics and its teaching and learning. What is valued in drawing-based research in mathematics education, and what kind of knowledge is produced through student drawings, however, is not known. This study examines drawing-based research studies to understand these questions by applying a novel framework - the legitimation code theory (LCT). The study focuses on two cases: one of which looked at middle school students' images of mathematicians (draw a mathematician) and the other examined the same age group students' descriptions of mathematics classrooms (draw a mathematics classroom). Within both studies, greater emphases are on the students' perceptions relating to the discipline-related issues such as teaching and learning of mathematics, mathematics classroom experiences, and practices and tools of mathematicians. Students' perceptions of the mathematics discipline and their attitudes toward mathematics and perceptions of the attributes of mathematicians are also a focus. The study offers the LCT approach to critically analyze the drawing-based research in the mathematics education field to contribute to the production of significant and needed knowledge in the field.
Driven by Innovation 2.0 (the information age, the innovation form of the knowledge society), the form evolved by the Internet development, giving rise to economic and social development ("Internet +"). With this background, a novel approach is presented for fostering excellence in physicians, aligning with the contemporary demands of our era. Self-administered questionnaire was used to facilitate the collection of data on medical advanced mathematics course offerings, distribution of teaching hours of each major and the perception of the course teaching system in 11 medical universities in China. The distribution of course offerings in each major was analyzed, and one-sample t-test was conducted on the perspectives of course offerings, content settings (theoretical & practical), educational objectives, teaching reforms, and Synthetical Sensation (SS) of the curriculum system and educational model. The study included various specialties such as clinical medicine, pharmacy, public health, health management, and life sciences, all of which offered advanced mathematics course. The content of medical mathematics textbooks was designed to meet the practical needs of relevant professions, and encompass online laboratory classes and social practice. However, a noticeable misalignment was observed between the content of medical mathematics courses and the realistic requirements of professions (t = -3.614~-3.018, P < 0.05). The perceived difference in the completeness of curriculum systems was not significantly apparent. There was a difference in the perception of the effectiveness of teaching reforms (t = -4.485, P < 0.05), and there was a difference in the perception of the synthesis of the educational model in all cases (t = -5.067, P < 0.05). There are localized differences in curricula, and the number of course hours is basically reasonable; course content needs to be updated; the implementation of course objectives is not in place; the curriculum system can meet the needs of talent training; the innovation of the education model needs to be put into practice; and there are obvious differences in the comprehensive cognition of the teaching system and the education model. Based on the analysis of the problems, we build a new STC teaching mode with smart classroom based on "professional needs, practical needs and requirements for cultivating excellent physician talents".
Driving is a complex daily activity indicating age and disease-related cognitive declines. Therefore, deficits in driving performance compared with ones without mild cognitive impairment (MCI) can reflect changes in cognitive functioning. There is increasing evidence that unobtrusive monitoring of older adults' driving performance in a daily-life setting may allow us to detect subtle early changes in cognition. The objectives of this paper include designing low-cost in-vehicle sensing hardware capable of obtaining high-precision positioning and telematics data, identifying important indicators for early changes in cognition, and detecting early-warning signs of cognitive impairment in a truly normal, day-to-day driving condition with machine learning approaches. Our statistical analysis comparing drivers with MCI to those without reveals that those with MCI exhibit smoother and safer driving patterns. This suggests that drivers with MCI are cognizant of their condition and tend to avoid erratic driving behaviors. Furthermore, our Random Forest models identified the number of night trips, number of trips, and education as the most influential factors in our data evaluation.
Drosophila is a crucial biological experimental teaching material extensively utilized in experimental teaching. In this experimental teaching, each student typically needs to manually identify hundreds of fruit flies and record multiple of each fly. This task involves substantial workload, and the classification standards can be inconsistent. To address this issue, we introduce a deep convolutional neural network that classifies the traits of every fruit fly, using a two-stage consisting of an object detector and a trait classifier. We propose a keypoint-assisted classification model with tailored training session for the trait classification task and significantly enhanced the model interpretability. Additionally, we've enhanced the RandAugment method to better fit the features of our task. The model is trained with progressive learning and adaptive regularization under limited computational resources. The final classification model, which utilizes MobileNetV3 as backbone, achieves an accuracy of 97.5%, 97.5% and 98% for the eyes, wings, gender tasks, respectively. After optimization, the model is highly lightweight, classifying 600 fruit fly traits from raw images in 10 seconds and having a size less than 5 MB. It can be easily deployed on any android device. The development of this system is conducive to promoting the experimental teaching, such as verifying genetic laws with Drosophila as the research object. It can also be used for scientific research involving a large number of Drosophila classifications, statistics and analyses.
Drowning is a global problem. This so-called "silent killer" claims hundreds of thousands of lives every year around the world. Despite this, beaches are a fundamental space in the lives of many people throughout the world. The scant knowledge regarding aspects of safety, accident prevention and the identification of risks in these natural aquatic environments, all contribute to the number of aquatic accidents that happen. The objective of this study was to establish the reality about the Spain population's knowledge of risks on the beaches. A study was carried out using a questionnaire, generated through Google Forms©. The questionnaire was available for 7 days, from May 12 to 19, 2022. 289 responses were selected. The Chi-square statistic was used to analyze the associations while Cramér's V statistics and the Odds Ratio were applied to analyze their strength. The significance level was p < 0.05 for the entire study. 50.5% of the participants knew how to correctly identify a rip current. In terms of definition, identification and response to a current, those who were lifeguards were more likely than the general population to answer correctly. Sex, age or place of residence do not seem to explain knowledge about rip currents in an active population group. Finally, as a factor in avoiding possible accidents related to currents, the importance of having professional lifeguards in natural aquatic environments is confirmed.
Drowning is the cause of approximately 4,000 U.S. deaths each year and disproportionately affects some age, racial, and ethnic groups. Infrastructure disruptions during the COVID-19 pandemic, including limited access to supervised swimming settings, might have affected drowning rates and risk. Data on factors that contribute to drowning risk are limited. To assess the potential impact of the pandemic on drowning death rates, pre- and post-COVID-19 pandemic rates were compared. National Vital Statistics System data were used to compare unintentional drowning death rates in 2019 (pre-COVID-19 pandemic onset) with those in 2020, 2021, and 2022 (post-pandemic onset) by age, sex, and race and ethnicity. National probability-based online panel survey (National Center for Health Statistics Rapid Surveys System) data from October-November 2023 were used to describe adults' self-reported swimming skill, swimming lesson participation, and exposure to recreational water. Unintentional drowning death rates were significantly higher during 2020, 2021, and 2022 compared with those in 2019. In all years, rates were highest among children aged 1-4 years; significant increases occurred in most age groups. The highest drowning rates were among non-Hispanic American Indian or Alaska Native and non-Hispanic Black or African American persons. Approximately one half (54.7%) of U.S. adults reported never having taken a swimming lesson. Swimming skill and swimming lesson participation differed by age, sex, and race and ethnicity. Recent increases in drowning rates, including those among populations already at high risk, have increased the urgency of implementing prevention strategies. Basic swimming and water safety skills training can reduce the risk for drowning. Addressing social and structural barriers that limit access to this training might reduce drowning deaths and inequities. The U.S. National Water Safety Action Plan provides recommendations and tools for communities and organizations to enhance basic swimming and water safety skills training.
Dual phase technetium-99mTc-methoxy isobutyl isonitrile (MIBI) single-photon emission computed tomography with computed tomography (SPECT/CT) may be the most accurate conventional imaging approach for localization of enlarged parathyroid gland (EPG). The imaging is based on the radiopharmaceutical (RP) retention in EPG compared to washout from normal thyroid and normal parathyroid glands. This study aimed to estimate and optimize the contribution of computed tomography (CT) scan and scan range to effective dose (ED) in dual-phase MIBI SPECT/CT parathyroid scintigraphy. The study included seventy-four patients; thirty-seven with reduced and thirty-seven with extended CT scan range. The ED caused by the CT scan was calculated using Dose Length Product (DLP) data and estimated using the Imaging Performance Assessment of CT scanners (ImPACT) calculator. For all patients, the contribution of CT to the ED in a combined SPECT/CT examination was 2.62 ± 0.29 mSv (48%). The contribution of CT to the total ED was 1.8 ± 0.18 mSv (33%) when using reduced and 3.44 ± 0.23 mSv (64%) when using extended scan range. The DLP and ED were statistically significantly different between the reduced and extended CT scan range (p < 0.001) in the first and second phases. The individual organ dose was reduced from 8% to 94%. The hybrid SPECT/CT improves the interpretation of nuclear medicine images and also increases the radiation dose to the patient. An adequately defined CT scan range on SPECT/CT imaging, can significantly reduce a patient's ED. The research findings showed that knowledge of anatomy, pathology and technology can provide optimising diagnostic procedures and reduce patient ED after SPECT/CT scans.
Dual-process theories of higher order cognition (DPTs) have been enjoying much success, particularly since Kahneman's 2002 Nobel prize address and recent book Thinking, Fast and Slow (2009). Historically, DPTs have attempted to provide a conceptual framework that helps classify and predict differences in patterns of behavior found under some circumstances and not others in a host of reasoning, judgment, and decision-making tasks. As evidence has changed and techniques for examining behavior have moved on, so too have DPTs. Killing two birds with one stone, Evans and Stanovich (2013, this issue) respond to five main criticisms of DPTs. Along with addressing each criticism in turn, they set out to clarify the essential defining characteristics that distinguish one form of higher order cognition from the other. The aim of this commentary is to consider the defining characteristics of Type 1 and Type 2 processing that have been proposed and to suggest that the evidence can be taken to support quantitative differences rather than qualitatively distinct processes.