Yoxas/statistical_literacy huggingface dataset:
Case-based learning enables the application of theory to practice using real-life patient cases. The present study aims to compare the effect between video cases and paper cases on motivation for learning and knowledge acquisition to perform a clinical reasoning case exercise by occupational therapy students. A mixed-methods design was used with 120 students randomized into two groups. All students conducted a clinical reasoning case exercise on the same case, although in different presentation formats: paper case and video case. The quantitative measures of this study were the scores of motivation for learning from the Instructional Material Motivation Survey and the grades of a clinical reasoning case exercise. The qualitative part was based on the positive and negative aspects perceived by the participants. The results showed that the motivation for learning was significantly higher for the video case compared to the paper case, although there were no differences in the grades of the clinical reasoning case exercise between the two groups. The video cases were perceived as more relevant to professional practice and more informative in terms of non-verbal communication and context. In light of the results, teachers could use these two formats of presentation of cases with different objectives.
Case-Based Reasoning (CBR) is a form of analogical reasoning in which the solution for a (new) query case is determined using a database of previous known cases with their solutions. Cases similar to the query are retrieved from the database, and then their solutions are adapted to the query. In medicine, a case usually corresponds to a patient and the problem consists of classifying the patient in a class of diagnostic or therapy. Compared to "black box" algorithms such as deep learning, the responses of CBR systems can be justified easily using the similar cases as examples. However, this possibility is often under-exploited and the explanations provided by most CBR systems are limited to the display of the similar cases. In this paper, we propose a CBR method that can be both executed automatically as an algorithm and presented visually in a user interface for providing visual explanations or for visual reasoning. After retrieving similar cases, a visual interface displays quantitative and qualitative similarities between the query and the similar cases, so as one can easily classify the query through visual reasoning, in a fully explainable manner. It combines a quantitative approach (visualized by a scatter plot based on Multidimensional Scaling in polar coordinates, preserving distances involving the query) and a qualitative approach (set visualization using rainbow boxes). We applied this method to breast cancer management. We showed on three public datasets that our qualitative method has a classification accuracy comparable to k-Nearest Neighbors algorithms, but is better explainable. We also tested the proposed interface during a small user study. Finally, we apply the proposed approach to a real dataset in breast cancer. Medical experts found the visual approach interesting as it explains why cases are similar through the visualization of shared patient characteristics.
Case-control studies are observational studies in which cases are subjects who have a characteristic of interest, such as a clinical diagnosis, and controls are (usually) matched subjects who do not have that characteristic. After cases and controls are identified, researchers "look back" to determine what past events (exposures), if any, are significantly associated with caseness. For "looking back," data may be obtained by clinical history-taking or from medical records such as case files or large electronic health care databases. The data are analyzed using logistic regression, which adjusts for confounding variables and yields an odds ratio and a probability value for the association between the exposure of interest (independent variable) and caseness (dependent variable). Because case-control studies are not randomized controlled studies, cause-effect relationships do not necessarily explain significant associations detected in the regressions; unexplored confounding may be responsible. These concepts are explained with the help of examples.
Cataclysm refers to a large-scale and violent event involving disaster, catastrophe, calamity, and tragedy. The recent surge of a pandemic (coronavirus disease 2019 (COVID-19)), humanitarian crisis in Sudan, civil unrest, and war, such as the attacks in Syria and Ukraine, reiterate the need to address the trauma attached to such catastrophes. This study focuses on adapting positive psychology, one of the newest branches of psychology to emerge, to help human beings prosper and lead healthy, happy lives. It also explores post-traumatic stress disorder (PTSD) and means to overcome traumatizing psychological dejection caused by such mishaps. Demoralization is the immediate effect caused on victims and survivors of such devastations. A comprehensive study of cataclysm and demoralization is concomitant with the suffering produced by various factors pertaining to mass annihilation. A deductive methodology is incorporated into the qualitative data to analyze the oral testimonies of war and disaster survivors in Svetlana Alexievich's works titled Zinky Boys: Soviet Voices from the Afghanistan War, Second Hand Time, The Unwomanly Face of War and Chernobyl Prayer. Interpretation of the polyphonic voices in the oral sagas of interviewees in Alexievich's works reveals an increased level of anxiety, stress, and trauma post-catastrophic events. The implementation of positive psychology has been identified as an appropriate psychological intervention to address the issues related to psychological devastation.
Catalytic micromotors can be used to detect molecules of interest in several ways. The straightforward approach is to use such motors as sensors of their "fuel" (i.e., of the species consumed for self-propulsion). Another way is in the detection of species which are not fuel but still modulate the catalytic processes facilitating self-propulsion. Both of these require analysis of the motion of the micromotors because the speed (or the diffusion coefficient) of the micromotors is the analytical signal. Alternatively, catalytic micromotors can be used as the means to enhance mass transport, and thus increase the probability of specific recognition events in the sample. This latter approach is based on "classic" (e.g., electrochemical) analytical signals and does not require an analysis of the motion of the micromotors. Together with a discussion of the current limitations faced by sensing concepts based on the speed (or diffusion coefficient) of catalytic micromotors, we review the findings of the studies devoted to the analytical performances of catalytic micromotor sensors. We conclude that the qualitative (rather than quantitative) analysis of small samples, in resource poor environments, is the most promising niche for the catalytic micromotors in analytical chemistry.
Categorical data analysis of 2 × 2 contingency tables is extremely common, not least because they provide risk difference, risk ratio, odds ratio, and log odds statistics in medical research. A χ2 test analysis is most often used, although some researchers use likelihood ratio test (LRT) analysis. Does it matter which test is used? A review of the literature, examination of the theoretical foundations, and analyses of simulations and empirical data are used by this paper to argue that only the LRT should be used when we are interested in testing whether the binomial proportions are equal. This so-called test of independence is by far the most popular, meaning the χ2 test is widely misused. By contrast, the χ2 test should be reserved for where the data appear to match too closely a particular hypothesis (e.g., the null hypothesis), where the variance is of interest, and is less than expected. Low variance can be of interest in various scenarios, particularly in investigations of data integrity. Finally, it is argued that the evidential approach provides a consistent and coherent method that avoids the difficulties posed by significance testing. The approach facilitates the calculation of appropriate log likelihood ratios to suit our research aims, whether this is to test the proportions or to test the variance. The conclusions from this paper apply to larger contingency tables, including multi-way tables.
Catheter-associated urinary tract infections (CAUTIs) account for the majority of device-associated healthcare-acquired infections with significant morbidity and mortality worldwide. In developing countries with limited resources, the burden of CAUTI have substantial burden owing to the lack of well-organized infection prevention and control. Although there are studies in African countries, the magnitude of CAUTI is inconsistent. Therefore this systematic review and meta-analysis aimed to determine the pooled prevalence of CAUTI in Africa and identify the pathogens involved. Systematic review of articles from different databases and search engines such as Medline/PubMed, Google Scholar, Science Direct, and African Journal online were systematically searched to identify potential studies. Data were extracted on Microsoft Excel spreadsheet and analyzed using STATA 17.0. The pooled prevalence of CAUTI was estimated using a random effects model, inverse of variance was used to assess statistical heterogeneity across studies. Egger's tests was performed to identify possible publication bias. This systematic review and meta-analysis incorporated twenty studies, revealing a pooled prevalence of CAUTI at 43.28%. Gram-negative bacteria were the leading cause of CAUTI accounts for 82.9%. Escherichia coli (45.06%) was the most frequent gram-negative bacterial isolate involved in CAUTI followed by Klebsiella spp (24.17%). Staphylococcus aureus was the predominant gram-positive bacterial isolate, accounting for 53.24% of gram-positive associated cases in CAUTI. In conclusion, the high prevalence of CAUTI in Africa underlines a pressing healthcare challenge. Addressing this issue requires a concerted effort, encompassing health education, infection prevention measures, resource allocation, and collaborative initiatives to enhance patient safety and mitigate the impact of CAUTI on healthcare systems in the region. As prolonged catheterization increases the risk of infection, catheters should only be used for proper indications and removed promptly when no longer needed.
Causal epidemiology for regulatory risk analysis seeks to evaluate how removing or reducing exposures would change disease occurrence rates. We define interventional probability of causation (IPoC) as the change in probability of a disease (or other harm) occurring over a lifetime or other specified time interval that would be caused by a specified change in exposure, as predicted by a fully specified causal model. We define the closely related concept of causal assigned share (CAS) as the predicted fraction of disease risk that would be removed or prevented by a specified reduction in exposure, holding other variables fixed. Traditional approaches used to evaluate the preventable risk implications of epidemiological associations, including population attributable fraction (PAF) and the Bradford Hill considerations, cannot reveal whether removing a risk factor would reduce disease incidence. We argue that modern formal causal models coupled with causal artificial intelligence (CAI) and realistically partial and imperfect knowledge of underlying disease mechanisms, show great promise for determining and quantifying IPoC and CAS for exposures and diseases of practical interest. We briefly review key CAI concepts and terms and then apply them to define IPoC and CAS. We present steps to quantify IPoC using a fully specified causal Bayesian network (BN) model. Useful bounds for quantitative IPoC and CAS calculations are derived for a two-stage clonal expansion (TSCE) model for carcinogenesis and illustrated by applying them to benzene and formaldehyde based on available epidemiological and partial mechanistic evidence. Causal BN models for benzene and risk of acute myeloid leukemia (AML) incorporating mechanistic, toxicological and epidemiological findings show that prolonged high-intensity exposure to benzene can increase risk of AML (IPoC of up to 7e-5, CAS of up to 54%). By contrast, no causal pathway leading from formaldehyde exposure to increased risk of AML was identified, consistent with much previous mechanistic, toxicological and epidemiological evidence; therefore, the IPoC and CAS for formaldehyde-induced AML are likely to be zero. We conclude that the IPoC approach can differentiate between likely and unlikely causal factors and can provide useful upper bounds for IPoC and CAS for some exposures and diseases of practical importance. For causal factors, IPoC can help to estimate the quantitative impacts on health risks of reducing exposures, even in situations where mechanistic evidence is realistically incomplete and individual-level exposure-response parameters are uncertain. This illustrates the strength that can be gained for causal inference by using causal models to generate testable hypotheses and then obtaining toxicological data to test the hypotheses implied by the models-and, where necessary, refine the models. This virtuous cycle provides additional insight into causal determinations that may not be available from weight-of-evidence considerations alone.
Causal inference lies at the core of many scientific endeavours. Yet answering causal questions is challenging, especially when studying culture as a causal force. Against this backdrop, this paper reviews research designs and statistical tools that can be used - together with strong theory and knowledge about the context of study - to identify the causal impact of culture on outcomes of interest. We especially discuss how overlooked strategies in cultural evolutionary studies can allow one to approximate an ideal experiment wherein culture is randomly assigned to individuals or entire groups (instrumental variables, regression discontinuity design, and epidemiological approach). In doing so, we also review the potential outcome framework as a tool to engage in causal reasoning in the cultural evolutionary field.
Ceftriaxone is a broad spectrum of widely used antibiotics as it is highly effective against Gram-negative and Gram-positive isolates. Research on Ceftriaxone resistance helps to know its current status. Hence, we aimed to identify the prevalence of Ceftriaxone resistance on bacteria isolated from clinical specimens among patients at GAMBY Teaching General Hospital Bahir Dar, Ethiopia. Hospital-based retrospective cross-sectional study was conducted at GAMBY teaching General Hospital from November 01, 2015, to December 30, 2020, on 402 clinical specimens. Ceftriaxone susceptibility tests were carried out using the Kirby-Bauer disc diffusion method. Descriptive statistics and chi-square tests were applied for the analysis. Escherichia coli 114 (28.4%), S. aureus 90 (22.4%), S. saprophyticus 42 (10.4%), and Klebsiella spp 42 (10.4%) were the predominant identified bacteria. The overall resistance of ceftriaxone was 230 (57.2%). Sex and type of the clinical specimens had significantly associated with its resistance whereas age was not associated with ceftriaxone resistance. Ceftriaxone resistance has been steadily increasing. Ceftriaxone resistance was high. Sex and type of the clinical specimens had significantly associated with its resistance. Prevention mechanisms to ceftriaxone resistance should be strictly implemented. The right drugs should be selected based on susceptibility patterns.
Cellular metabolism is characterized by an intricate network of interactions between biochemical fluxes, metabolic compounds and regulatory interactions. To investigate and eventually understand the emergent global behavior arising from such networks of interaction is not possible by intuitive reasoning alone. This contribution seeks to describe recent computational approaches that aim to asses the topological and functional properties of metabolic networks. In particular, based on a recently proposed method, it is shown that it is possible to acquire a quantitative picture of the possible dynamics of metabolic systems, without assuming detailed knowledge of the underlying enzyme-kinetic rate equations and parameters. Rather, the method builds upon a statistical exploration of the comprehensive parameter space to evaluate the dynamic capabilities of a metabolic system, thus providing a first step towards the transition from topology to function of metabolic pathways. Utilizing this approach, the role of feedback mechanisms in the maintenance of stability is discussed using minimal models of cellular pathways.
Central aortic BP may predict cardiovascular outcomes better than upper arm brachial BP. In recent years, technology has enabled central BP estimation by recording a peripheral BP waveform from a standard upper arm cuff. The accuracy of these devices is not well documented, and this study aimed to address this issue. This study was a systematic review, conducted according to PRISMA guidelines, of observational studies published between 2008 and 2023 that reported accuracy testing of cuff-based central BP devices, compared with reference invasive aortic BP. The primary analysis was stratified according to each commercially available device. Pooled estimates were calculated using random-effects models based on mean differences and standard errors. Six thousand four hundred and fifteen studies were screened, and 27 studies met inclusion criteria (plus one unpublished study). This generated data for seven devices that are commercially available, which were tested among 2125 adult participants. There was very high heterogeneity when all devices were pooled (I2 = 97.5%), and, when stratified by device, the accuracy of estimated central BP was highly device-dependent (range of accuracy across different devices -12.4 mmHg (-16.3 to -8.5) to 3.2 mmHg (0.2-6.1). Two of the seven commercially available devices had not undergone external validation testing. The accuracy of commercially available cuff-based central BP devices is highly device-specific and not all are accurate for the estimation of central SBP. These findings have major implications for the appropriate interpretation of studies that use cuff-based estimated central BP.
Cerebral infarction (CI) is a common cerebrovascular disease worldwide, and the burden caused by the sequelae of CI has increased significantly. However, current treatment guidelines lack standardized recommendations for pharmacotherapy of sequelae of CI. This retrospective study collected and analyzed 1.98 million prescriptions concerning sequelae of CI from patients admitted to Zhiyun Health Internet Hospital in 2022. The mean age of patients was 66.2 ± 11.4 years, and 52.40% were male. 79.73% had one or more comorbidities. For treatment, the prescriptions of 1-, 2- and ≥ 3-drug accounted for 64.55%, 23.77% and 11.68% respectively. Chinese patent medicine (CPM) prescriptions, western medicine (WM) prescriptions, and CPM and WM combined (CPM + WM) prescriptions accounted for 53.81%, 27.33%, and 18.86% respectively. In CPM prescriptions, the most frequently prescribed medications were Salvia miltiorrhiza (34.81%), Ginkgo biloba (24.96%), Panax notoginseng (20.67%), Gastrodia (7.15%) and Ligusticum Wallichii (4.90%). For WM prescriptions, the most commonly prescribed agents were anti-hypertensive (32.82%), anti-thrombotic (16.06%), vasodilator (15.70%), anti-dementia (10.88%), and lipid-lowering (9.58%) drugs. Among CPM + WM prescriptions, 72.61% had CPM/WM = 1, 21.20% had CPM/WM < 1, and 6.19% had CPM/WM > 1. This research utilized real-world data extracted from internet hospitals in China to present valuable evidence of online prescription patterns among patients experiencing sequelae of CI.
Cervical artery dissections (CAD) are a common cause of ischemic cerebrovascular events among the younger and middle-aged population. Altogether, CAD counts for up to 15% of all causes of stroke in patients aged 50 or younger. Among the known etiological causes, especially addressing the younger population with mechanical traumas and whiplash injuries are regarded as the main culprits. However, cases of spontaneous dissection are also widespread, with risk factors such as hypertension, migraine, and lifestyle factors increasing the risk of occurrence. Clinically, the symptoms associated with a cerebrovascular event caused by CADs are highly variable and can be classified as either compressive symptoms (such as Horner's syndrome and cervical pain) or stroke syndromes attributable to cerebral ischemia. Therefore, establishing an early diagnosis might be particularly challenging as it requires particular attention and quick clinical reasoning when interviewing the patient. With these certain particularities, our main focus was to conduct a prospective study involving up to 54 patients who were diagnosed with CAD in our clinical facility between January 2015 and December 2022, with the focus of assessing certain individual parameters attributable to each patient and their influence and prognosis value for their short and long term evolution. An important emphasis was placed on parameters such as topographical localization, clinical presentation, severity of the questioned cerebrovascular event, outcomes, and causative factors. Statistical validity tools were applied when possible.
Cervical auscultation is a simple, noninvasive method for diagnosing dysphagia, although the reliability of the method largely depends on the subjectivity and experience of the evaluator. Recently developed methods for the automatic detection of swallowing sounds facilitate a rough automatic diagnosis of dysphagia, although a reliable method of detection specialized in the peculiar feature patterns of swallowing sounds in actual clinical conditions has not been established. We investigated a novel approach for automatically detecting swallowing sounds by a method wherein basic statistics and dynamic features were extracted based on acoustic features: Mel Frequency Cepstral Coefficients and Mel Frequency Magnitude Coefficients, and an ensemble learning model combining Support Vector Machine and Multi-Layer Perceptron were applied. The evaluation of the effectiveness of the proposed method, based on a swallowing-sounds database synchronized to a video fluorographic swallowing study compiled from 74 advanced-age patients with dysphagia, demonstrated an outstanding performance. It achieved an F1-micro average of approximately 0.92 and an accuracy of 95.20%. The method, proven effective in the current clinical recording database, suggests a significant advancement in the objectivity of cervical auscultation. However, validating its efficacy in other databases is crucial for confirming its broad applicability and potential impact.