Yoxas/statistical_literacy huggingface dataset:
A 'proof-of-concept' version of a software tool for making transparent predictions of acute aquatic toxicity has been developed. It is primarily limited to semi-quantitative predictions in one species, the ciliated protozoan, Tetrahymena pyriformis. A freely available system, 'Eco-Derek', was derived by adapting a well-established, knowledge-based structure-activity and reasoning platform (Derek for Windows, Lhasa Limited). The Derek reasoning code was modified to express potency rather than confidence. Structure-activity relationship (SAR) development utilised a curated version of a published dataset, supplemented with the CADASTER Challenge datasets. Forty-five structural alerts were produced. The dependence on log P was examined for each alert and entered into the system as qualitative reasoning rules specifying the predicted potency as Very Low, Low, Moderate, High or Very High. Evaluation studies showed: (a) moderate accuracy for the training set but low accuracy for an external test set; (b) non-linearity in the toxicity-log P relationship for chemicals without identified structural alerts; (c) insufficient differentiation of substituent effects in some of the reactivity-based structural alerts resulting in too few chemicals predicted with Very High toxicity; and (d) the need for additional structural alerts covering polar narcosis and less common reactive or metabolically activated chemical functionality.
A "critical access point in the STEM pipeline for Latinx students and other students of color" (Herrera et al., 2018), community colleges provide a seminal breeding ground for academic pursuits (Bahr et al., 2017). However, how personal networks influence STEM pathways of two-year college students remains largely unexplored. This mixed methods case study explores influence of personal networks on pursuing STEM fields via social network analysis and qualitative narratives. 36 women of color STEM majors at a two-year urban Hispanic-Serving Institution were interviewed via social network questionnaire. Participants nominated anyone who has influenced their STEM trajectory, which signifies influence to their reason for pursuing a STEM path; they also had an option to qualitatively elaborate on any nomination but this was not required. Nominations were counted towards degree centrality and categorized into social relationships. Participants nominated diverse relationship influences, with family as the most influential relationship group, followed by college faculty/staff. Qualitative narratives revealed that family influenced participants, regardless of relatives' educational attainment level at the high school or lower level. In alignment with community cultural wealth, family members provided the impetus for pursuing STEM pathways through influence on participants' (1) aspirational capital, (2) familial capital, and (3) resistant capital.
A 4-week summer preceptorship offered first-year medical students early exposure to anesthesiology following their preclerkship courses in pharmacology and cardiopulmonary physiology. The main objective was to provide students with clinical skills and prepare them for rotations while immersing them in a unique experience, introducing a specialty not covered in core rotations. Participants were selected via their responses to an application. In addition to shadowing anesthesiologists, curriculum components included weekly faculty lectures on core aspects of anesthesiology (introductory basics, perioperative drugs, airway, and crisis management); multiple simulation labs (workstation setup, intubation clinics, intravenous and central line placement, ultrasound techniques); research opportunities; and mentorship from physicians and senior medical students. The program culminated in each student successfully leading a simulated case to receive a certificate of completion. A survey of 15 participants revealed significant improvements in ability to intubate (P < 0.05), ability to perform a transthoracic echocardiogram (P < 0.05), interest in anesthesiology as a specialty (P < 0.05), and preparedness for future rotations (P < 0.05). This program accelerates clinical exposure for preclerkship students, providing insights into anesthesiology early in their medical journey. It provides research and mentorship, fosters professional growth, and enhances individual competitiveness for residency program applications.
A capstone course often serves as the final checkpoint of student readiness before the commencement of experiential training. The purpose of this study was to determine if the assessment components from the capstone course can serve as predictors of student performance during their Advanced Pharmacy Practice Experiences (APPEs). Student data was analyzed to observe the correlation between performance in the Pharmacy Curriculum Outcomes Assessment (PCOA), student performance in the capstone course, and the overall grade point average (GPA) earned during APPEs. Spearman rank correlation analysis, multiple linear regression, and Mann-Whitney U test were used for statistical analysis. A statistically significant positive correlation was observed between the overall APPE GPA and students' capstone course grade, top drug competency exam score, pharmacy calculation competency exam score, and PCOA exam score. A significant regression equation was obtained during the analysis: (F(5, 97) = 5.62, p < 0.001), with an R2 = 0.225 (adjusted R2 = 0.185). In the linear regression model, capstone GPA emerged as a significant predictor (β = 0.155; p = 0.019) of APPE GPA amongst the tested variables. Additionally, students scoring < 73% on the top drug competency exam in the capstone course or less than the reference group in the PCOA exam were found to have significantly lower GPA during their APPEs compared to other students. Performance on the top drug competency exam and the PCOA exam can serve as potential predictors of success during APPEs.
A central goal of modern evidence-based medicine is the development of simple and easy to use tools that help clinicians integrate quantitative information into medical decision-making. The Bayesian Pre-test/Post-test Probability (BPP) framework is arguably the most well known of such tools and provides a formal approach to quantify diagnostic uncertainty given the result of a medical test or the presence of a clinical sign. Yet, clinical decision-making goes beyond quantifying diagnostic uncertainty and requires that that uncertainty be balanced against the various costs and benefits associated with each possible decision. Despite increasing attention in recent years, simple and flexible approaches to quantitative clinical decision-making have remained elusive. We extend the BPP framework using concepts of Bayesian Decision Theory. By integrating cost, we can expand the BPP framework to allow for clinical decision-making. We develop a simple quantitative framework for binary clinical decisions (e.g., action/inaction, treat/no-treat, test/no-test). Let p be the pre-test or post-test probability that a patient has disease. We show that r*=(1-p)/p represents a critical value called a decision boundary. In terms of the relative cost of under- to over-acting, r* represents the critical value at which action and inaction are equally optimal. We demonstrate how this decision boundary can be used at the bedside through case studies and as a research tool through a reanalysis of a recent study which found widespread misestimation of pre-test and post-test probabilities among clinicians. Our approach is so simple that it should be thought of as a core, yet previously overlooked, part of the BPP framework. Unlike prior approaches to quantitative clinical decision-making, our approach requires little more than a hand-held calculator, is applicable in almost any setting where the BPP framework can be used, and excels in situations where the costs and benefits associated with a particular decision are patient-specific and difficult to quantify.
A challenge for medical educators is to provide learning opportunities that allow students to develop technical and non-technical skills as set by the clearly defined learning objectives within their relevant institutions. This is particularly relevant in clinical education, which encompasses a vast majority of medical education. Specifically, clinical education is highly variable, with numerous distractions, interruptions and variability in learning experience and quality of clinical educators which often result in underprepared medical students by the time they transition into clinical practice. Simulation-based teaching (SBT) has been a key pedagogical approach that has been implemented into curriculum design to assist with addressing some of these educational challenges. However, their implementation is highly variable, and research into evidence-based best practice considerations in SBT design and implementation is fundamental to their success in medical student development. A narrative review was performed following a computer-assisted search on electronic databases Medline, Embase and Google Scholar. Relevant papers that explored the role of SBT in medical education were considered for this review. SBT is an important pedagogical approach to support the education of medical students. Their use has the benefit of providing a standardised and safe environment that mimics 'real life' as a means of allowing students to hone key skills with respect to clearly defined learning outcomes. The role of debriefing and feedback is crucial to the development of efficacious SBT programs, and therefore the upskilling and training of educators is a key aspect of evidence-based SBT design. Despite this, medical educators must be cognisant of the limitations of SBT. These include the cost and resources required to develop and implement SBT sessions, the effort and conceptualisation required to standardise and ensure these programs reflect real-life situations as well as the degree of training for facilitators to ensure they can best deliver and achieve learning outcomes and provide effective debriefing and feedback for students. Understanding the educational frameworks and the evidence-based best practice principles for SBT design and implementation is highly necessary for medical educators given the resource demands of SBT programs.
A challenge in accurately identifying and classifying left ventricular hypertrophy (LVH) is distinguishing it from hypertrophic cardiomyopathy (HCM) and Fabry disease. The reliance on imaging techniques often requires the expertise of multiple specialists, including cardiologists, radiologists, and geneticists. This variability in the interpretation and classification of LVH leads to inconsistent diagnoses. LVH, HCM, and Fabry cardiomyopathy can be differentiated using T1 mapping on cardiac magnetic resonance imaging (MRI). However, differentiation between HCM and Fabry cardiomyopathy using echocardiography or MRI cine images is challenging for cardiologists. Our proposed system named the MRI short-axis view left ventricular hypertrophy classifier (MSLVHC) is a high-accuracy standardized imaging classification model developed using AI and trained on MRI short-axis (SAX) view cine images to distinguish between HCM and Fabry disease. The model achieved impressive performance, with an F1-score of 0.846, an accuracy of 0.909, and an AUC of 0.914 when tested on the Taipei Veterans General Hospital (TVGH) dataset. Additionally, a single-blinding study and external testing using data from the Taichung Veterans General Hospital (TCVGH) demonstrated the reliability and effectiveness of the model, achieving an F1-score of 0.727, an accuracy of 0.806, and an AUC of 0.918, demonstrating the model's reliability and usefulness. This AI model holds promise as a valuable tool for assisting specialists in diagnosing LVH diseases.
A challenge in solving the genotype-to-phenotype relationship is to predict a cell's metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.
A challenge in the pandemic era is to implement effective but flexible practical teaching for biological sciences courses. Such teaching needs to deliver conceptual, analytical and practical skills training while having the option to rapidly respond to health and safety issues, local regulations, staff and student concerns. In this paper, we describe a set of cell biology practicals (mini-project) that meets many of these requirements and provides flexibility in providing skills training both through online and in practical laboratory environments. We have used a human adenocarcinoma cell line A431 stably transfected with a fluorescent cell cycle reporter as a biological model to deliver training through discrete work packages encompassing cell culture, fluorescence microscopy, biochemistry and statistics. How such work packages can be modified to, an online format either partially or completely is also described. Furthermore, the activities can be adapted for teaching both undergraduate and postgraduate level courses to ensure effective skills training which is applicable to a wide range of biological degree programs and levels of study.
A childhood cancer diagnosis is a traumatic experience for patients and their families. However, little is known about the effect on grandparents. We aimed to investigate the negative psychosocial impact, coping strategies, and positive outcomes of grandparents of childhood cancer patients in Switzerland. We collected data using a semi-structured interview guide and applied qualitative content analysis. We conducted 20 interviews with 23 grandparents (57% female; mean age = 66.9 years; SD = 6.4; range = 57.0-82.4) of 13 affected children (69% female; mean age = 7.5 years; SD = 6.1; range = 1.0-18.9) between January 2022 and April 2023. The mean time since diagnosis was 1.0 years (SD = 0.5; range = 0.4-1.9). Grandparents were in shock and experienced strong feelings of fear and helplessness. They were particularly afraid of a relapse or late effects. The worst part for most was seeing their grandchild suffer. Many stated that their fear was always present which could lead to tension and sleep problems. To cope with these negative experiences, the grandparents used internal and external strategies, such as accepting the illness or talking to their spouse and friends. Some grandparents also reported positive outcomes, such as getting emotionally closer to family members and appreciating things that had previously been taken for granted. Grandparents suffer greatly when their grandchild is diagnosed with cancer. Encouragingly, most grandparents also reported coping strategies and positive outcomes despite the challenges. Promoting coping strategies and providing appropriate resources could reduce the psychological burden of grandparents and strengthen the whole family system.
A close relationship between math performance and math anxiety has been demonstrated, but how different presentations of mathematics are associated with math anxiety has not been investigated. This study recruited 826 elementary school students in grades 5 and 6. All students were given math trait and state anxiety questionnaires; a nonverbal matrix reasoning task; and verbalized, symbolic, and situational fraction problem-solving tasks. After data cleaning, 475 boys and 323 girls (798 in total) were included in the analysis (mean age = 11.79, SD = 0.82). Partial correlation analysis showed that students' math traits and state anxiety were more closely related to symbolic fraction problem-solving than to verbalized and situational fraction problem-solving. Mixed linear model analysis showed that math state anxiety for symbolic problem-solving was significantly greater than that for verbalized and situational problem-solving. Based on these findings, we concluded that the presentation of symbolic math is more likely to induce math anxiety than verbalized or situational math. This finding has potential practical applications in math anxiety interventions and education.
A common application of differential expression analysis is finding genes that are differentially expressed upon treatment in only one out of several groups of samples. One of the approaches is to test for significant difference in expression between treatment and control separately in the two groups, and then select genes that show statistical significance in one group only. This approach is then often combined with a gene set enrichment analysis to find pathways and gene sets regulated by treatment in only this group. Here we show that this procedure is statistically incorrect and that the interaction between treatment and group should be tested instead. Moreover, we show that gene set enrichment analysis applied to such incorrectly defined genes group-specific genes may result in misleading artifacts. Due to the presence of false negatives, genes significant in one, but not the other group are enriched in gene sets which correspond to the overall effect of the treatment. Thus, the results appear related to the problem at hand, but do not reflect the group-specific effect of a treatment. A literature search revealed that more than a quarter of papers which used a Venn diagram to illustrate the results of separate differential analysis have also applied this incorrect reasoning.
A common problem in meta-analyses is the unavailability of mean and standard deviation (SD). Unfortunately, only having values of the median, interquartile range (IQR), or range cannot be directly utilized for meta-analysis. Although some estimation and conversion methods have been proposed in the past two decades, there were no published and user-friendly tools developed based on multiple scenarios of missing SD. Therefore, this study aimed to provide a collection of possible circumstances of missing sample means or SD with solutions for teaching and research. A total of 10 common circumstances of missing SD or mean could have available statistics of p value, t value, z score, confidence interval, standard error, median, IQR, and range. Teachers and investigators can use relevant formulas for finding the sample mean and SD according to the available circumstance. Due to the complicated computations, our team provides a free available spreadsheet. With ever-evolving statistical methods, some formulas may be further improved in the future; therefore, it is recommended to involve statisticians in evidence-based practice or systematic reviews.
A complete quantitative account of P. Wason's (1966) abstract selection task is proposed. The account takes the form of a mathematical model. It is assumed that some response patterns are caused by inferential reasoning, whereas other responses reflect cognitive processes that affect each card selection separately and independently of other card selections. The model parameters assess the contributions of different interpretational, inferential, and heuristic factors that jointly determine performance in the selection task. The interpretation of most of the model parameters in terms of these different factors is validated experimentally. This model of the selection task is the first to account for the observed frequencies of all 16 possible response patterns that can arise.
A comprehensive analysis was performed, considering blockchain technology (BT) properties in digital health, addressing medicolegal, privacy, and regulatory considerations. Adherence to personal data protection and healthcare regulatory guidelines were analyzed and compared for GDPR (Europe), HIPAA (United States), CCPA (California), PIPEDA (Canada), the Privacy Act of 1988 (Australia), APPI (Japan), and LGPD (Brazil). Issues such as health systems, strengthening and aligning policy orientations and initiatives, and emphasizing the role of data analysis in shaping health policies were explored. The study addressed conflicts between the legal frameworks and blockchain, comparing and suggesting solutions like the revision of laws and the integration of compliance mechanisms. Additionally, it sought to enhance IT-health literacy by integrating the healthcare and legal domains. Ongoing collaboration between legal, health, and IT experts is essential for designing systems that effectively balance privacy rights and data protection while maximizing the benefits of disruptive technologies like blockchain.
A comprehensive definition of culture encompasses shared norms, beliefs, expectations, language, and customs, all of which are crucial considerations when working with patients with limited English proficiency (LEP). In this study, the authors examined how language, external influences, and patient-provider relational factors associated with decisional conflict in prenatal care patients. The authors conducted a cross-sectional study to assess decisional conflict related to postpartum contraception, elective induction, and newborn feeding methods. The survey included questions about demographics, communication methods, external influences, and provider trust, and was distributed to prenatal care patients who spoke either English or Spanish. Data analysis involved using descriptive statistics and chi-square analyses. Out of the 23 respondents, 12 were Spanish-speaking and 11 were English-speaking. Spanish-speaking participants were less likely to have health insurance compared to English-speaking participants (χ2(1, N = 23) = 3.67, p = 0.016). There was no statistically significant difference in decisional conflict between English- and Spanish-speaking participants. Religion affected 11 of 23 participants' decisions, while partner expectations influenced 10 of 23 participants. Working with an interpreter and the quality of interpretation were crucial for Spanish-speaking individuals. Most participants (59%) felt that the provider's understanding of the patient's cultural background was important for decision-making. While there was no association between language discordance and decisional conflict, several factors influencing prenatal decision-making were identified. The use and quality of interpretation significantly affected decision-making and should be prioritized for patients with LEP. Religion and partner expectations were found to be highly influential in decision-making. Respondents also emphasized the importance of the provider's understanding of the patient's cultural background.