Yoxas/statistical_literacy huggingface dataset:
Precision in evaluating underweight and overweight status among children and adolescents is paramount for averting health and developmental issues. Existing standards for these assessments have faced scrutiny regarding their validity. This study investigates the age and height dependencies within the international standards set by the International Obesity Task Force (IOTF), relying on body mass index (BMI), and contrasts them with Japanese standards utilizing the percentage of overweight (POW). We scrutinized a comprehensive database comprising 7,863,520 children aged 5-17 years, sourced from the School Health Statistics Research initiative conducted by Japan's Ministry of Education, Culture, Sports, Science, and Technology. Employing the quantile regression method, we dissected the structure of weight-for-height distributions across different ages and sexes, quantifying the potentially biased assessments of underweight and overweight status by conventional criteria. Applying IOFT criteria for underweight assessment revealed pronounced height dependence in males aged 11-13 and females aged 10-11. Notably, a discernible bias emerged, wherein children in the lower 25th percentile were classified as underweight five times more frequently than those in the upper 25th percentile. Similarly, the overweight assessment displayed robust height dependence in males aged 8-11 and females aged 7-10, with children in the lower 25th percentile for height deemed obese four or five times more frequently than their counterparts in the upper 25th percentile. Furthermore, using the Japanese POW criteria for assessment revealed significant age dependence in addition to considerably underestimating the percentage of underweight and overweight cases under the age of seven. However, the height dependence for the POW criterion was smaller than the BMI criterion, and the difference between height classes was less than 3-fold. Our findings underscore the intricacies of age-dependent changes in body composition during the growth process in children, emphasizing the absence of gold standards for assessing underweight and overweight. Careful judgment is crucial in cases of short or tall stature at the same age, surpassing sole reliance on conventional criteria results.
Precision medicine (PM) is in great progressive stages in the West and allows healthcare practitioners (HCPs) to give treatment according to the patient's genetic findings, physiological and environmental characteristics. PM is a relatively new treatment approach in Pakistan Therefore, it is important to investigate the level of awareness, attitude, and challenges faced by oncology physicians while practicing PM for various therapies, especially cancer treatment. The present study aims to explore the level of awareness, attitude, and practice of PM in Pakistan along with the challenges faced by the oncologists for the treatment of cancer using the PM approach. Phenomenology-based qualitative approach was used. Face-to-face in-depth interviews were conducted using the purposive sampling approach among oncologists in Lahore, Pakistan. The data were analyzed using thematic content analysis to identify themes and sub-themes. Out of 14 physicians interviewed 11 were aware of PM. They were keen on training to hone their skills and agreed on providing PM. Oncologists believed PM was expensive and given to affluent patients only. Other impeding factors include cost, lack of knowledge, and drug unavailability. Despite basic knowledge and will to practice, resource and cost constraints were marked as significant barriers. Additional training programs and inclusion into the curriculum may help to pave the way to PM implementation in the future. In addition, health authorities and policymakers need to ensure a cheaper PM treatment can be made available for all cancer patients.
Precision medicine (PM) raises a key question: How do we know what works when the number of people with a health problem becomes small or one (n = 1)? We here present a formative case from Norway. The Norwegian Board of Health Supervision was faced with a cancer patient, who had improved after treatment with a drug in the private health sector but was refused continued treatment in the public health service due to lack of clinical trial evidence. The Board overturned this decision, arguing that the drug had been unambiguously documented to work in the individual case. We aim to provide an in-depth analysis of this case and The Board's decision and thereby to illustrate and elucidate key epistemological and ethical issues and developments in PM. We provide our analysis and discussion using tools of critical thinking and concepts from philosophy of science and medicine, such as uncertainty, evidence, forms of inference and causation. We also examine the case in light of the history of evidence-based medicine (EBM). The case reflects an epistemological shift in medicine where PM puts greater emphasis on evidence that arises in individual patients after the treatment is provided over pre-existing population-based evidence. PM may rely more heavily on abduction to decide what works and qualitative, rather than quantitative judgements. The case also illustrates a possible shift in the concept of causation from regularity accounts to mechanistic and process accounts. We discuss the ethical implications of a shift from more 'traditional' to 'personalised EBM'. A framework that is more based on abductions and evidence arising in the individual case has problems in creating quantifiable, reliable and generalisable evidence, and in promoting transparency and accountability. PM currently lacks clear criteria for deciding what works in an individual, posing ethical challenges.
Preclinical simulation is an evidence-based method for nurse practitioner (NP) students to improve clinical communication and disease management competency. During simulation, students may receive feedback from multiple sources, including standardized patients (SPs), faculty, peers, and themselves. Although evidence supports simulation with multisource feedback, its impact on clinical knowledge and communication has yet to be evaluated among NP students. We designed, implemented, and evaluated a preclinical simulation program with structured multisource feedback integrated into a disease management course within a Doctor of Nursing Practice curriculum. Differences in communication self-efficacy and disease management knowledge before and after participation, as well as perceptions of learning and importance of varying feedback sources, were evaluated using a single group pre-post mixed-methods design. On average, clinical communication self-efficacy was significantly higher, and disease management knowledge scores were significantly higher after participation. Learners rated feedback sources as important or very important and described varying feedback sources as complementary. Feedback from SPs, peers, learners themselves, and faculty was complementary and important to learning. This preclinical simulation program with purposeful integration of multisource feedback provides an evidence-based foundation for scaffolding multidomain competency development into curriculums to meet updated standards of advanced nursing education.
Preclinical studies reveal that the microbiome broadly affects immune responses and the deposition and/or clearance of amyloid-beta (Aβ) in mouse models of Alzheimer's disease (AD). Whether the microbiome shapes central and peripheral immune profiles in AD models remains unknown. We examined adaptive immune responses in two mouse models containing AD-related genetic predispositions (3xTg and 5xFAD) in the presence or absence of the microbiome. T and B cells were altered in brain-associated and systemic immune tissues between genetic models and wildtype mice, with earlier signs if inflammation in female mice. Systemic immune responses were modulated by the microbiome and differed by sex. Further, the absence of a microbiome in germ-free mice resulted in reduced cognitive deficits, primarily in female mice. These data reveal sexual dimorphism in early signs of inflammation and the effects of the microbiome, and highlight a previously unrecognized interaction between sex and the microbiome in mouse models of AD. Systemic review: We reviewed the literature related to Alzheimer's disease (AD), inflammation, and the microbiome using PubMed. We cite several studies that demonstrate the influence of the microbiome on inflammation and cognitive performance in both animal models and humans. However, the mechanisms linking immunity to AD are not well understood. Interpretation: Using two well-established mouse models of AD, we found that the microbiome does not strongly influence the onset of inflammation in brain-draining lymph nodes; rather, it largely modulates systemic immune responses, local cytokine production, and cognitive performance. Notably, the inflammatory state in mice was affected by sex, and this sex effect differed between local and systemic tissues and mice with or without a microbiome. Future directions: Our work identified a sex- and microbiome-mediated effect on inflammation and cognitive performance. Future studies may focus on microbiome-dependent mechanisms that intersect with sex hormone and immune responses to determine peripheral effects on AD outcomes. Adaptive immunity is activated at early ages and differentially by sex in mouse models of AD.Inflammation in 5xFAD mice is characterized by increased IL-17A-producing T cells.Inflammation in 3xTg mice is characterized by increased cytokine responses in males, but attenuated cytokine responses in female mice.Longitudinal immune responses differ between 3xTg mice and 5xFAD mice.Both 3xTg and 5xFAD female mice show improved learning and cognition in the absence of a microbiome.
Preconception health (PCH), which describes the health status of reproductive-aged individuals, can influence reproductive, maternal, and fetal/neonatal outcomes. PCH disparities have been observed in certain populations, prompting the development of tailored resources. Our objective was to compare the PCH characteristics of women with and without disabilities. We undertook a secondary analysis of the 2003-2014 cycles of the Canadian Community Health Survey (CCHS), which included n = 115,295 women aged 15-49 years. Among these, we also examined a sub-sample of n = 12,495 women with a subsequent obstetrical delivery identified in a linkage of the CCHS with the Discharge Abstract Database (DAD, 2003-2017). The outcomes were 8 PCH indicators. We used modified Poisson regression to estimate adjusted prevalence ratios (aPRs) for each PCH indicator, comparing women with and without disabilities, and multinomial logistic regression to calculate adjusted odds ratios for 1, 2, and ≥ 3 PCH indicators (vs. 0). Analyses were adjusted for baseline demographics. Reproductive-aged women with disabilities had significantly increased aPRs of smoking (1.42 [95% CI:1.37-1.48]), obesity (1.57 [1.48-1.65]), and self-reported fair/poor physical (5.56 [5.09-6.07]) and mental health (4.07 [3.71-4.47]), compared to those without disabilities. They were also more likely to have ≥ 3, 2, and 1 PCH indicators (vs. 0). Findings were similar in the sub-sample with a subsequent obstetrical delivery. Canadian reproductive-aged women with disabilities experience important PCH disparities. Further research is needed to inform tailored education and resources to support PCH in individuals with disabilities, in combination with policies to address structural barriers to PCH.
Prediabetes has been associated with increased all-cause and cardiovascular mortality. However, no large-scale studies have been conducted in Mexico or Latin America examining these associations. We analyzed data from 115,919 adults without diabetes (diagnosed or undiagnosed) aged 35-84 years who participated in the Mexico City Prospective Study between 1998 and 2004. Participants were followed until January 1st, 2021 for cause-specific mortality. We defined prediabetes according to the American Diabetes Association (ADA, HbA1c 5.7% to 6.4%) and the International Expert Committee (IEC, HbA1c 6.0-6.4%) definitions. Cox regression adjusted for confounders was used to estimate all-cause and cause-specific mortality rate ratios (RR) at ages 35-74 years associated with prediabetes. During 2,085,392 person-years of follow-up (median in survivors 19 years), there were 6,810 deaths at ages 35-74, including 1,742 from cardiovascular disease, 892 from renal disease and 108 from acute diabetic crises. Of 110,405 participants aged 35-74 years at recruitment, 28,852 (26%) had ADA-defined prediabetes and 7,203 (7%) had IEC-defined prediabetes. Compared with those without prediabetes, individuals with prediabetes had higher risk of all-cause mortality at ages 35-74 years (RR 1.13, 95% CI 1.07-1.19 for ADA-defined prediabetes and RR 1.28, 1.18-1.39 for IEC-defined prediabetes), as well as increased risk of cardiovascular mortality (RR 1.22 [1.10-1.35] and 1.42 [1.22-1.65], respectively), renal mortality (RR 1.35 [1.08-1.68] and 1.69 [1.24-2.31], respectively), and death from an acute diabetic crisis (RR 2.63 [1.76-3.94] and 3.43 [2.09-5.62], respectively). RRs were larger at younger than at older ages, and similar for men compared to women. The absolute excess risk associated with ADA and IEC-defined prediabetes at ages 35-74 accounted for6% and 3% of cardiovascular deaths respectively, 10% and 5% of renal deaths respectively, and 31% and 14% of acute diabetic deaths respectively. Prediabetes is a significant risk factor for all-cause, cardiovascular, renal, and acute diabetic deaths in Mexican adults. Identification and timely management of individuals with prediabetes for targeted risk reduction could contribute to reducing premature mortality from cardiometabolic causes in this population. Wellcome Trust, the Mexican Health Ministry, the National Council of Science and Technology for Mexico, Cancer Research UK, British Heart Foundation, UK Medical Research Council. Instituto Nacional de Geriatría (Mexico City).
Prediabetes risk assessment models derived from large sample sizes are scarce. To establish a robust assessment model for prediabetes and to validate the model in different populations. The China National Diabetes and Metabolic Disorders Study (CNDMDS) collected information from 47325 participants aged at least 20 years across China from 2007 to 2008. The Thyroid Disorders, Iodine Status and Diabetes Epidemiological Survey (TIDE) study collected data from 66108 participants aged at least 18 years across China from 2015 to 2017. A logistic model with stepwise selection was performed to identify significant risk factors for prediabetes and was internally validated by bootstrapping in the CNDMDS. External validations were performed in diverse populations, including populations of Hispanic (Mexican American, other Hispanic) and non-Hispanic (White, Black and Asian) participants in the National Health and Nutrition Examination Survey (NHANES) in the United States and 66108 participants in the TIDE study in China. C statistics and calibration plots were adopted to evaluate the model's discrimination and calibration performance. A set of easily measured indicators (age, education, family history of diabetes, waist circumference, body mass index, and systolic blood pressure) were selected as significant risk factors. A risk assessment model was established for prediabetes with a C statistic of 0.6998 (95%CI: 0.6933 to 0.7063) and a calibration slope of 1.0002. When externally validated in the NHANES and TIDE studies, the model showed increased C statistics in Mexican American, other Hispanic, Non-Hispanic Black, Asian and Chinese populations but a slightly decreased C statistic in non-Hispanic White individuals. Applying the risk assessment model to the TIDE population, we obtained a C statistic of 0.7308 (95%CI: 0.7260 to 0.7357) and a calibration slope of 1.1137. A risk score was derived to assess prediabetes. Individuals with scores ≥ 7 points were at high risk of prediabetes, with a sensitivity of 60.19% and specificity of 67.59%. An easy-to-use assessment model for prediabetes was established and was internally and externally validated in different populations. The model had a satisfactory performance and could screen individuals with a high risk of prediabetes.
Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1 measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data are expensive), and the complex relationships between anomalies not easily captured using traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model was deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines.
Predicting biological systems' behaviors requires taking into account many molecular and genetic elements for which limited information is available past a global knowledge of their pairwise interactions. Logical modeling, notably with Boolean Networks (BNs), is a well-established approach that enables reasoning on the qualitative dynamics of networks. Several dynamical interpretations of BNs have been proposed. The synchronous and (fully) asynchronous ones are the most prominent, where the value of either all or only one component can change at each step. Here we prove that, besides being costly to analyze, these usual interpretations can preclude the prediction of certain behaviors observed in quantitative systems. We introduce an execution paradigm, the Most Permissive Boolean Networks (MPBNs), which offers the formal guarantee not to miss any behavior achievable by a quantitative model following the same logic. Moreover, MPBNs significantly reduce the complexity of dynamical analysis, enabling to model genome-scale networks.
Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.
Predicting rain from large-scale environmental variables remains a challenging problem for climate models and it is unclear how well numerical methods can predict the true characteristics of rainfall without smaller (storm) scale information. This study explores the ability of three statistical and machine learning methods to predict 3-hourly rain occurrence and intensity at 0.5° resolution over the tropical Pacific Ocean using rain observations the Global Precipitation Measurement (GPM) satellite radar and large-scale environmental profiles of temperature and moisture from the MERRA-2 reanalysis. We also separated the rain into different types (deep convective, stratiform, and shallow convective) because of their varying kinematic and thermodynamic structures that might respond to the large-scale environment in different ways. Our expectation was that the popular machine learning methods (i.e., the neural network and random forest) would outperform a standard statistical method (a generalized linear model) because of their more flexible structures, especially in predicting the highly skewed distribution of rain rates for each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are common problems in climate models. One implication of this study is that machine learning tools must be carefully assessed and are not necessarily applicable to solving all big data problems. Another implication is that traditional climate model approaches are not sufficient to predict extreme rain events and that other avenues need to be pursued.
Predicting students' aptitude for post-secondary success remains a widely studied topic. This descriptive study explored demographic variables contributing to success in quantitative courses required by the nursing degree plan. Identification of an "at risk" student profile may inform interventions with which to support attainment of an academic degree. The purpose of this study was to examine the associations between demographic characteristics and successful completion of baccalaureate nursing courses thought to enhance quantitative reasoning skills: first-year math, first-year chemistry, and second-year pathopharmacology nursing. This retrospective analysis accessed 4521 academic records of students who took these three courses at a United States university sometime between Fall 2008 and Fall 2015. De-identified student data included course grades, gender, full-time study, income, marital status, first generation, secondary school (also known as high school) location, dual credit, and high school and university grade point averages. Descriptive statistical analysis was conducted to describe the important features of the data. Of the 4521 records, 2556 undergraduates (57%) passed the courses in which they were enrolled. Among successful students, females outnumbered males (66%), ages ranged from 20 to 24 years, 86% were classified as low income, 54% fit the designation of first generation, and 12% earned dual credit (university credit during secondary school). Our data demonstrate a positive relationship between dual credit and success, with the strongest correlation (0.62) noted for students in pathopharmacology. In the baccalaureate-nursing plan of study, courses thought to enhance students' quantitative reasoning skills remain difficult for some to successfully complete. We conclude that the more successful students tend to be older, have a higher income, and a higher high school grade point average, while those less successful are directly out of high school and have not earned dual credit.