Yoxas/statistical_literacy huggingface dataset:
In the year 2021, human brucellosis ranked fifth in terms of the number of cases among all statutorily notifiable infectious diseases in China, thus remaining a significant concern for public health. This study aims to provide insights into the financial burden of human brucellosis by examining hospital stays and associated costs for affected individuals. In this retrospective study, we gathered updated data from 467 inpatient cases primarily diagnosed with human brucellosis at eight major tertiary hospitals in Beijing, China, spanning from 2013 to 2023. To comprehensively explore the economic impact on individuals, we not only analyzed the duration of hospital stays and total costs but also examined various charge types, including drug, lab test, medical imaging, medical treatment, surgical procedures, medical supplies and consumables, inpatient bed care, nursing services, and other services costs. Statistical analysis was employed to compare differences among gender, age, ethnicity, type of health insurance, condition at admission, comorbidity index, the performance of surgery, and the site of infection. Both the length of stay and total cost exhibited significant variations among insurance, surgery, and infection site groups. Utilization categories demonstrated significant differences between patients who underwent surgery and those who did not, as well as across different infection sites. Furthermore, multiple linear regression analysis revealed that the condition at admission, Elixhauser comorbidity index, infection site, and surgery influenced both hospital stay and total cost. In addition, age and insurance type were associated with total costs. By delving into various utilization categories, we have addressed a significant gap in the literature. Our findings provide valuable insights for optimizing the allocation and management of health resources based on the influencing factors identified in this study.
In their classic accounts, anthropological ethnographers developed causal arguments for how specific sociocultural structures and processes shaped human thought, behavior, and experience in particular settings. Despite this history, many contemporary ethnographers avoid establishing in their work direct causal relationships between key variables in the way that, for example, quantitative research relying on experimental or longitudinal data might. As a result, ethnographers in anthropology and other fields have not advanced understandings of how to derive causal explanations from their data, which contrasts with a vibrant "causal revolution" unfolding in the broader social and behavioral sciences. Given this gap in understanding, we aim in the current article to clarify the potential ethnography has for illuminating causal processes related to the cultural influence on human knowledge and practice. We do so by drawing on our ongoing mixed methods ethnographic study of games, play, and avatar identities. In our ethnographic illustrations, we clarify points often left unsaid in both classic anthropological ethnographies and in more contemporary interdisciplinary theorizing on qualitative research methodologies. More specifically, we argue that for ethnographic studies to illuminate causal processes, it is helpful, first, to state the implicit strengths and logic of ethnography and, second, to connect ethnographic practice more fully to now well-developed interdisciplinary approaches to causal inference. In relation to the first point, we highlight the abductive inferential logic of ethnography. Regarding the second point, we connect the ethnographic logic of abduction to what Judea Pearl has called the ladder of causality, where moving from association to intervention to what he calls counterfactual reasoning produces stronger evidence for causal processes. Further, we show how graphical modeling approaches to causal explanation can help ethnographers clarify their thinking. Overall, we offer an alternative vision of ethnography, which contrasts, but nevertheless remains consistent with, currently more dominant interpretive approaches.
In this article, an improved slime mould algorithm (SMA-CSA) is proposed for solving global optimization and the capacitated vehicle routing problem (CVRP). This improvement is based on the mixed-strategy optimization of Cauchy mutation and simulated annealing to alleviate the lack of global optimization capability of the SMA. By introducing the Cauchy mutation strategy, the optimal solution is perturbed to increase the probability of escaping from the local extreme value; in addition, the annealing strategy is introduced, and the Metropolis sampling criterion is used as the acceptance criterion to expand the global search space to enhance the exploration phase to achieve optimal solutions. The performance of the proposed SMA-CSA algorithm is evaluated using the CEC 2013 benchmark functions and the capacitated vehicle routing problem. In all experiments, SMA-CSA is compared with ten other state-of-the-art metaheuristics. The results are also analyzed by Friedman and the Wilcoxon rank-sum test. The experimental results and statistical tests demonstrate that the SMA-CSA algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the capacitated vehicle routing problem demonstrate its efficiency and discrete solving ability.
In this article, the authors discuss the development and current curriculum of the Art in Medicine course at the University of Colorado School of Medicine (CUSOM), developed in collaboration with the Centre for Bioethics and Humanities, and the Denver Art Museum. Examples of art museum-based techniques implemented with medical students are discussed with objectives, methods, and pedagogy highlighted. Translational skillsets for medical professionals, including empathy, tolerance of ambiguity, perspective taking, bias, and communication are emphasised through practice in engagement in visual arts with facilitation by medical faculty and art museum educators. Following the discussion of classroom based activities, two of the co-authors, physician-educators from the CUSOM Departments of Medicine and Radiology, discuss the direct applications and importance of these skillsets in the context of the exam room and the reading room respectively.
In this article, we analyze "digital massification" in smart cities, that is, an ever-growing number of market participants, consumers, and Internet of Things devices with simultaneous accommodation of users to increasing disturbances and inconveniences due to data congestion-as a driver for systemic risk. We argue that digital massification phenomena largely escape societal awareness due to their protracted evolution and are therefore still in the blind spot of long-term governance. Our analysis makes methodological use of historical and relational analogy, and we introduce and elaborate concepts and terms that allow us to discuss the evolutionary nature of massification, that is, the foreseeable increasing probability of the occurrence of trigger events. Using the analogy to the history of road traffic congestion, we deduce that digital massification will most likely lead to a future "risk transition" where tolerated disturbances and inconveniences of the present will turn into systemic impacts. This insight calls for heightened sensitivity in governance to massification phenomena to ensure the long-term resilience of smart cities.
In this article, we describe a collaboration between a university and school district aimed at preparing more students who are typically underidentified and underserved in gifted programs for advanced STEM coursework in high school. Features of the collaboration included early intervention, significant outside-of-school programming and coursework in STEM through elementary and middle school (over 400 h), tutoring, and support for families. Data included scores on two standardized achievement tests for 14 cohorts of students who self-identified as African American and/or Hispanic (361 total students) as they progressed from Grade 3 to Grade 8, placement in accelerated mathematics courses in Grade 9, and college matriculation. Comparisons were made to data for comparable demographic groups within the school district. Results showed greater growth in reading and mathematics compared to demographic peers in the district reaching achievement levels comparable to the highest-scoring students in the district. There were significant increases in the percentage of students entering high school with advanced placement in mathematics as well as improvement over multiple cohorts in matriculation at more selective institutions of higher education. Results are discussed with respect to best practices for the identification of students with potential STEM talent, effective university-school partnerships, and STEM-talent development pathways.
In this article, we discuss the embodiment of infinity as one of fundamental concepts in mathematics. In contrast to the embodiment of many other mathematical concepts, the embodiment of infinity is an endless dynamic process. In embodying +∞, an object moves rightward toward a previously-set limit and passes it. Then, a new limit is set on the right side of the moving object. The moving object continues its movement and passes it as well. The moving object can pass any limit. In other words, there is no impassable limit for it. In embodying -∞, a similar process happens but the movement is leftward. Embodiment of infinitely small quantities has a basic similarity to the embodiment of infinitely large quantities, although it is different in some respects. We call the embodiment of infinity as iterative embodiment. It is iterative because the process of setting a new limit and passing it is repeated endlessly. Finally, it is suggested that in the process of embodying infinitely large and infinitely small quantities, the visual system and the motor system play important roles, as this process involves spatial concepts and movement.
In this article, we introduce a novel moment closure scheme based on concepts from model predictive control (MPC) to accurately describe the time evolution of the statistical moments of the solution of the chemical master equation (CME). The method of moments, a set of ordinary differential equations frequently used to calculate the first nm moments, is generally not closed since lower-order moments depend on higher-order moments. To overcome this limitation, we interpret the moment equations as a nonlinear dynamical system, where the first nm moments serve as states, and the closing moments serve as the control input. We demonstrate the efficacy of our approach using three example systems and show that it outperforms existing closure schemes. For polynomial systems, which encompass all mass-action systems, we provide probability bounds for the error between true and estimated moment trajectories. We achieve this by combining the convergence properties of a priori moment estimates from stochastic simulations with guarantees for nonlinear reference tracking MPC. Our proposed method offers an effective solution to accurately predict the time evolution of moments of the CME, which has wide-ranging implications for many fields, including biology, chemistry, and engineering.
In this article, we report on a typology of the demands of statistical and mathematical products (StaMPs) embedded in media items related to the COVID-19 (coronavirus) pandemic. The typology emerged from a content analysis of a large purposive sample of diverse media items selected from digital news sources based in four countries. The findings encompass nine categories of StaMPs: (1) descriptive quantitative information, (2) models, predictions, causality and risk, (3) representations and displays, (4) data quality and strength of evidence, (5) demographics and comparative thinking, (6) heterogeneity and contextual factors, (7) literacy and language demands, (8) multiple information sources, and (9) critical demands. We illustrate these categories via selected media items, substantiate them through relevant research literature, and point to categories that encompass new or enhanced types of demands. Our findings offer insights into the rich set of capabilities that citizens (including both young people and adults) must possess in order to engage these mass media demands, critically analyze statistical and mathematical information in the media, evaluate the meaning and credibility of news reports, understand public policies, and make evidenced-informed judgments. Our conclusions point to the need to revise current curricular frameworks and conceptual models (e.g., regarding statistical and probability literacy, adult numeracy), to better incorporate notions such as blended knowledge, vagueness, risk, strength of evidence, and criticality. Furthermore, more attention is needed to the literacy and language demands of media items involving statistical and mathematical information. Implications for further research and educational practice are discussed.
In this contribution, a general expression is derived for the probability density of the time to the most recent common ancestor (TMRCA) of a simple birth-death tree, a widely used stochastic null-model of biological speciation and extinction, conditioned on the constant birth and death rates and number of extant lineages. This density is contrasted with a previous result which was obtained using a uniform prior for the time of origin. The new distribution is applied to two problems of phylogenetic interest. First, that of the probability of the number of taxa existing at any time in the past in a tree of a known number of extant species, and given birth and death rates, and second, that of determining the TMRCA of two randomly selected taxa in an unobserved tree that is produced by a simple birth-only, or Yule, process. In the latter case, it is assumed that only the rate of bifurcation (speciation) and the size, or number of tips, are known. This is shown to lead to a closed-form analytical expression for the probability distribution of this parameter, which is arrived at based on the known mathematical form of the age distribution of Yule trees of a given size and branching rate, which is derived here de novo, and a similar distribution which additionally is conditioned on tree age. The new distribution is the exact Yule prior for divergence times of pairs of taxa under the stated conditions and is potentially useful in statistical (Bayesian) inference studies of phylogenies.
In this correspondence, we explain the reasoning for invalidity of the analysis choices by Kolberg et al., and provide the results produced using correct statistical procedures for their study design. Reassuringly, we could verify the original conclusions. That is, results of the corrected statistical models are similar to the results of the original analysis. Regardless of the magnitude of difference that corrected statistical methods make, results and conclusions that are derived from invalid methods are unsubstantiated. By verifying the results, we allow the readers to be assured that the published conclusions in the study by Kolberg et al. now rest on a sound evidential basis.
In this cross-sectional study, we evaluated the completeness, readability, and syntactic complexity of cardiovascular disease prevention information produced by GPT-4 in response to 4 kinds of prompts.
In this era of evidence-based medicine, only systematic research can help in providing judicious and precise healthcare to individual patients based on updated knowledge and skills. However, many medical professionals do not feel competent and confident enough to conduct research. One of the reasons could be the lack of a research-based curriculum in undergraduate courses. The National Medical Council has also stressed the need for formal training in research methodology for healthcare professionals. The research methodology workshops help to familiarize the participants with basic, clinical, and translational research required to impart optimum patient care. The objective of our study was to evaluate a research methodology workshop conducted for postgraduate students by assessing the participant's knowledge, feedback, and expected impact using Kirkpatrick's evaluation model. A quasi-experimental, single-group study was conducted among 132 first-year postgraduate students. The four levels of Kirkpatrick's model were applied for evaluation. Feedback forms, scores of the pretest and posttest, quality of the research proposal drafted by the postgraduates for their thesis, and finally successful submission of the research proposal were the components used to evaluate the four levels of outcome of Kirkpatrick's model. Data collected were compiled and tabulated into MS Excel. Proportions were calculated for categorical variables and mean and standard deviation (SD) for scores. A comparison of means between pre- and postworkshop scores was made with paired t-test. A value of P < 0.05 was considered statistically significant. Statistical analysis was done using IBM SPSS Statistics version 20.0 software. Out of 132 participants, 29% (38) were males and 71% (94) were females. The mean ± SD pretest and posttest scores at a 95% confidence interval were 10.55 ± 2.537 and 12.43 ± 2.484, respectively. The difference was found to be statistically significant by paired sample t-test (P < 0.001). Participant feedback is vital for improving research methodology workshops. The workshop met the overall requirements of the participants. There was a significant improvement in the knowledge of participants after the workshop completion.
In this letter, a new approach to compute free energy differences (FEDs) between multiple thermodynamics states is introduced. The method directly uses energy probability densities, which can be extracted with high accuracy from equilibrium simulations to obtain FEDs. Methods in current use, such as Bennett acceptance ratio (BAR), its multistate generalization (MBAR), or the weighted histogram analysis method (WHAM), require iterative solution of nonlinear equations which are known to be slowly convergent. The equations providing MBAR FEDs are identical to those derived earlier by Souaille and Roux in a method that has become known informally as "binless WHAM". In contrast, we obtain FEDs by solution of linear equations. For the classic two-state problem, the statistical error of our method, solving linear equations, is shown analytically to match that of BAR under common conditions.
In this letter, we highlight the considerable diversity in undergraduate dermatology training in the United Kingdom and acknowledge the barriers faced in implementing the revised national undergraduate curriculum provided by the British Association of Dermatologists (BAD). We provide a pragmatic approach of ensuring that our dermatology placement aligns with the BAD national undergraduate curriculum and Medical Licensing Assessment (MLA) content map, in the face of limited clinician time and placement length. We urge other medical schools to adopt our approach of curriculum mapping, particularly in light of the upcoming MLA, and sustainable educational resource development.
In this observational study, we aimed to describe the rounding structure in a high acuity neonatal intensive care unit (NICU) to identify potential barriers to efficient multidisciplinary rounds. We observed daily medical rounds (January-December 2018) on the resident teaching service in a 46-bed academic level IV NICU. Daily census, duration of rounds, and causes for rounding delays were recorded. During a subset of the study period, additional data were collected describing the time spent on specific activities and the room-to-room pathway followed by the rounding team. Descriptive statistics were used to summarize the census, total rounding time, time spent on each activity, and rounding time by day of the week and by attending. A total of 208 rounding days were observed. During the study period, the teaching service mean daily census was 17 patients and total rounding time (mean ± standard deviation) was 136 ± 31 minutes. Mean rounding time and time/patient varied between the nine attendings (total time range 109 minutes to 169 minutes, time/patient range 6.4 minutes/patient to 10.0 minutes/patient). In total, 91% of rounding time focused on patient care, teaching, and discussions with parents, while 9% of the time was spent deciding which patient to see next, moving between rooms, and waiting for members of the team to be ready to start rounds. On average, the medical team spent over 2 hours per day making multisciplinary rounds in the NICU with substantial variation between attending providers. While most time was spent on patient care, teaching, and talking with parents, we identified opportunities to improve rounding efficiency. · The structure of rounds in a NICU was observed to identify the potential barriers to efficiency.. · There are limited data on MDR processes in the NICU.. · In total, 9% of time was spent on patient care activities during daily rounds..
In this paper we investigate a variety of deep learning strategies for solving inverse problems. We classify existing deep learning solutions for inverse problems into three categories of Direct Mapping, Data Consistency Optimizer, and Deep Regularizer. We choose a sample of each inverse problem type, so as to compare the robustness of the three categories, and report a statistical analysis of their differences. We perform extensive experiments on the classic problem of linear regression and three well-known inverse problems in computer vision, namely image denoising, 3D human face inverse rendering, and object tracking, in presence of noise and outliers, are selected as representative prototypes for each class of inverse problems. The overall results and the statistical analyses show that the solution categories have a robustness behaviour dependent on the type of inverse problem domain, and specifically dependent on whether or not the problem includes measurement outliers. Based on our experimental results, we conclude by proposing the most robust solution category for each inverse problem class.
In this paper, a finite volume discretization scheme for partial integro-differential equations (PIDEs) describing the temporal evolution of protein distribution in gene regulatory networks is proposed. It is shown that the obtained set of ODEs can be formally represented as a compartmental kinetic system with a strongly connected reaction graph. This allows the application of the theory of nonnegative and compartmental systems for the qualitative analysis of the approximating dynamics. In this framework, it is straightforward to show the existence, uniqueness and stability of equilibria. Moreover, the computation of the stationary probability distribution can be traced back to the solution of linear equations. The discretization scheme is presented for one and multiple dimensional models separately. Illustrative computational examples show the precision of the approach, and good agreement with previous results in the literature.
In this paper, a method for vehicle collision analysis based on qualitative mechanics is presented. In collisions where accurate quantitative information is difficult to gather, this analytic method can act as a useful complement. Based on qualitative dynamic theories, reasonings of various factors including qualitative vector, collision behavior, translational and rotational motion are suggested. Using the law of momentum conservation within the frame of rigid-body dynamics, vehicle collision analyses based on this qualitative reasoning were developed into an accident analysis tool. This newly developed qualitative collision analysis tool was verified using 45 cases of vehicle-to-vehicle collision test data, and its practicality was proved by performing analyses of various vehicle collision accidents.