Yoxas/statistical_literacy huggingface dataset:
Argument schemes are abstractions substantiating the inferential connection between premise(s) and conclusion in argumentative communication. Identifying such conventional patterns of reasoning is essential to the interpretation and evaluation of argumentation. Whether studying argumentation from a theory-driven or data-driven perspective, insight into the actual use of argumentation in communicative practice is essential. Large and reliably annotated corpora of argumentative discourse to quantitatively provide such insight are few and far between. This is all the more true for argument scheme corpora, which tend to suffer from a combination of limited size, poor validation, and the use of ad hoc restricted typologies. In the current paper, we describe the annotation of schemes on the basis of two distinct classifications: Walton's taxonomy of argument schemes, and Wagemans' Periodic Table of Arguments. We describe the annotation procedure for each, and the quantitative characteristics of the resulting annotated text corpora. In doing so, we extend the annotation of the preexisting US2016 corpus of televised election debates, resulting in, to the best of our knowledge, the two largest consistently annotated corpora of schemes in argumentative dialogue publicly available. Based on evaluation in terms of inter-annotator agreement, we propose further improvements to the guidelines for annotating schemes: the argument scheme key, and the Argument Type Identification Procedure.
Around 2000 children are born in the UK per year with a neurodevelopmental genetic syndrome with significantly increased morbidity and mortality. Often little is known about expected growth and phenotypes in these children. Parents have responded by setting up social media groups to generate data themselves. Given the significant clinical evidence gaps, this research will attempt to identify growth patterns, developmental profiles and phenotypes, providing data on long-term medical and educational outcomes. This will guide clinicians when to investigate, monitor or treat symptoms and when to search for additional or alternative diagnoses. This is an observational, multicentre cohort study recruiting between March 2023 and February 2026. Children aged 6 months up to 16 years with a pathogenic or likely pathogenic variant in a specified gene will be eligible. Children will be identified through the National Health Service and via self-recruitment. Parents or carers will complete a questionnaire at baseline and again 1 year after recruitment. The named clinician (in most cases a clinical geneticist) will complete a clinical proforma which will provide data from their most recent clinical assessment. Qualitative interviews will be undertaken with a subset of parents partway through the study. Growth and developmental milestone curves will be generated through the DECIPHER website (https://deciphergenomics.org) where 5 or more children have the same genetic syndrome (at least 10 groups expected). The results will be presented at national and international conferences concerning the care of children with genetic syndromes. Results will also be submitted for peer review and publication.
Around 40%-70% of patients with multiple sclerosis (MS) may experience cognitive impairments during the course of their disease with detrimental effects on social and occupational activities. Transcranial direct current stimulation (tDCS has been investigated in pain, fatigue, and mood disorders related to MS, but to date, few studies have examined effects of tDCS on cognitive performance in MS. The current study aimed to investigate the effects of a multi-session tDCS protocol on cognitive performance and resting-state brain electrical activities in patients with MS. Twenty-four eligible MS patients were randomly assigned to real (anodal) or sham tDCS groups. Before and after 8 consecutive daily tDCS sessions over the left dorsolateral prefrontal cortex (DLPFC), patients' cognitive performance was assessed using the Cambridge Brain Sciences-Cognitive Platform (CBS-CP). Cortical electrical activity was also evaluated using quantitative electroencephalography (QEEG) analysis at baseline and after the intervention. Compared to the sham condition, significant improvement in reasoning and executive functions of the patients in the real tDCS group was observed. Attention was also improved considerably but not statistically significantly following real tDCS. However, no significant changes in resting-state brain activities were observed after stimulation in either group. Anodal tDCS over the left DLPFC appears to be a promising therapeutic option for cognitive dysfunction in patients with MS. Larger studies are required to confirm these findings and to investigate underlying neuronal mechanisms.
Art-based education is gaining interest in the medical field, particularly in specialties with a strong visual focus. Visual arts are increasingly used for the development of observational skills and social competencies. While content and objectives of art-based programs widely differ across medical faculties in the Netherlands, the diverse range of options underscore the interest in and the potential of this educational approach. In this report, we explore the value of art-based observational training for medical students and surgical residents in two prominent Dutch museums in Amsterdam and Rotterdam, respectively. Our program, conducted at the Rijksmuseum in Amsterdam and Depot Boijmans van Beuningen Museum in Rotterdam engaged medical students (n=24) and surgeons (in training) (n=66) in an interactive workshop focused on art observation led by an experienced art-educator and a clinical professional. Learning objectives were defined and a post-workshop questionnaire was devised to evaluate participants' perceptions, with a specific focus on contribution of the program to professional development. Both residents and surgeons acknowledged that the program had a positive impact on their professional skills. The program learned them to postpone their judgements and contributed to the awareness of their personal bias. Notably, medical students believed in the program's potential contribution to their professional development. Surgeons were more critical in their evaluation, emphasizing the challenge of sustainable improvement of skills within the limited duration of the course. An interactive art-based medical education program was offered to medical students, PhD students, house officers, surgical residents and surgeons in two well known Dutch museums. Participants expressed enthusiasm for the innovative educational approach they experienced at the museums. They learned about the importance of critical observation in their professional work, handling of ambiguity and got the opportunity to practice both observational and communicational skills in a creative manner. The findings indicate that medical students and surgical residents can benefit from art-based observational training, using art as a vehicle to develop their professional competencies.
Artemether-lumefantrine is widely used for uncomplicated Plasmodium falciparum malaria; sulfadoxine-pyrimethamine plus amodiaquine is used for seasonal malaria chemoprevention. We aimed to determine the efficacy of artemether-lumefantrine with and without primaquine and sulfadoxine-pyrimethamine plus amodiaquine with and without tafenoquine for reducing gametocyte carriage and transmission to mosquitoes. In this phase 2, single-blind, randomised clinical trial conducted in Ouelessebougou, Mali, asymptomatic individuals aged 10-50 years with P falciparum gametocytaemia were recruited from the community and randomly assigned (1:1:1:1) to receive either artemether-lumefantrine, artemether-lumefantrine with a single dose of 0·25 mg/kg primaquine, sulfadoxine-pyrimethamine plus amodiaquine, or sulfadoxine-pyrimethamine plus amodiaquine with a single dose of 1·66 mg/kg tafenoquine. All trial staff other than the pharmacist were masked to group allocation. Participants were not masked to group allocation. Randomisation was done with a computer-generated randomisation list and concealed with sealed, opaque envelopes. The primary outcome was the median within-person percent change in mosquito infection rate in infectious individuals from baseline to day 2 (artemether-lumefantrine groups) or day 7 (sulfadoxine-pyrimethamine plus amodiaquine groups) after treatment, assessed by direct membrane feeding assay. All participants who received any trial drug were included in the safety analysis. This study is registered with ClinicalTrials.gov, NCT05081089. Between Oct 13 and Dec 16, 2021, 1290 individuals were screened and 80 were enrolled and randomly assigned to one of the four treatment groups (20 per group). The median age of participants was 13 (IQR 11-20); 37 (46%) of 80 participants were female and 43 (54%) were male. In individuals who were infectious before treatment, the median percentage reduction in mosquito infection rate 2 days after treatment was 100·0% (IQR 100·0-100·0; n=19; p=0·0011) with artemether-lumefantrine and 100·0% (100·0-100·0; n=19; p=0·0001) with artemether-lumefantrine with primaquine. Only two individuals who were infectious at baseline infected mosquitoes on day 2 after artemether-lumefantrine and none at day 5. By contrast, the median percentage reduction in mosquito infection rate 7 days after treatment was 63·6% (IQR 0·0-100·0; n=20; p=0·013) with sulfadoxine-pyrimethamine plus amodiaquine and 100% (100·0-100·0; n=19; p<0·0001) with sulfadoxine-pyrimethamine plus amodiaquine with tafenoquine. No grade 3-4 or serious adverse events occurred. These data support the effectiveness of artemether-lumefantrine alone for preventing nearly all mosquito infections. By contrast, there was considerable post-treatment transmission after sulfadoxine-pyrimethamine plus amodiaquine; therefore, the addition of a transmission-blocking drug might be beneficial in maximising its community impact. Bill & Melinda Gates Foundation.
Arterial hypertension is one of the most prevalent chronic, non-communicable diseases and the leading preventable risk factor for cardiovascular disease (CVD) and all-cause mortality worldwide. Although its primary causes and consequences are preventable, it often remains undiagnosed. Consequently, this study aims to determine the prevalence and factors associated with normotensive, diagnosed, and undiagnosed hypertension in adults. A cross-sectional, population-based study was conducted in Sabaneta, Colombia, between 2021 and 2022, with 286 adults aged 18 and older. Stratified and systematic random sampling methods were employed. The World Health Organization STEP survey and the Perez Rojas test were utilized to assess behavioral risk factors and sedentary lifestyles. Body mass index, waist circumference, and arterial tension were measured using standardized instruments. The prevalence of hypertension was then estimated. Risk factors influencing normotensive, diagnosed, and undiagnosed hypertension were analyzed using multinomial regression. The outcome variable comprised three categories: normotensive (reference category), diagnosed hypertension, and undiagnosed hypertension. The multinomial regression coefficients were exponentiated and are presented as relative risk ratios (RRR) with 95% confidence intervals (CI). The model was adjusted for sex and sample weight per neighborhood. The study revealed a hypertension prevalence of 38.5% and an undiagnosed hypertension rate of 50.9%. Those with undiagnosed hypertension were predominantly adults over 60 years (RRR = 0.68; 95% CI: 0.53-0.86), individuals with an elementary school education (RRR = 1.75; 95% CI: 1.27-2.42), those physically active (RRR = 1.52; 95% CI: 1.22-1.89), without prior diagnoses of chronic comorbidities (RRR = 1.42; 95% CI: 1.12-1.82), and with obesity (RRR = 2.25; 95% CI: 1.63-3.11) or overweight conditions (RRR = 1.70; 95% CI: 1.334-2.15). Undiagnosed hypertension was significant among populations without risk conditions. There is an urgent need for community-based early detection and education strategies to mitigate this issue.
Artificial cells can be engineered to display dynamics sharing remarkable features in common with the survival behavior of living organisms. In particular, such active systems can respond to stimuli provided by the environment and undertake specific displacements to remain out of equilibrium, e.g. by moving towards regions with higher fuel concentration. In spite of the intense experimental activity aiming at investigating this fascinating behavior, a rigorous definition and characterization of such "survival strategies" from a statistical physics perspective is still missing. In this work, we take a first step in this direction by adapting and applying to active systems the theoretical framework of Transition Path Theory, which was originally introduced to investigate rare thermally activated transitions in passive systems. We perform experiments on camphor disks navigating Petri dishes and perform simulations in the paradigmatic active Brownian particle model to show how the notions of transition probability density and committor function provide the pivotal concepts to identify survival strategies, improve modeling, and obtain and validate experimentally testable predictions. The definition of survival in these artificial systems paves the way to move beyond simple observation and to formally characterize, design and predict complex life-like behaviors.
Artificial intelligence (AI) agents encounter the problem of catastrophic forgetting when they are trained in sequentially with new data batches. This issue poses a barrier to the implementation of AI-based models in tasks that involve ongoing evolution, such as cancer prediction. Moreover, whole slide images (WSI) play a crucial role in cancer management, and their automated analysis has become increasingly popular in assisting pathologists during the diagnosis process. Incremental learning (IL) techniques aim to develop algorithms capable of retaining previously acquired information while also acquiring new insights to predict future data. Deep IL techniques need to address the challenges posed by the gigapixel scale of WSIs, which often necessitates the use of multiple instance learning (MIL) frameworks. In this paper, we introduce an IL algorithm tailored for analyzing WSIs within a MIL paradigm. The proposed Multiple Instance Class-Incremental Learning (MICIL) algorithm combines MIL with class-IL for the first time, allowing for the incremental prediction of multiple skin cancer subtypes from WSIs within a class-IL scenario. Our framework incorporates knowledge distillation and data rehearsal, along with a novel embedding-level distillation, aiming to preserve the latent space at the aggregated WSI level. Results demonstrate the algorithm's effectiveness in addressing the challenge of balancing IL-specific metrics, such as intransigence and forgetting, and solving the plasticity-stability dilemma.
Artificial intelligence (AI) and machine learning (ML) are poised to have a substantial impact in the health care space. While a plethora of web-based resources exist to teach programming skills and ML model development, there are few introductory curricula specifically tailored to medical students without a background in data science or programming. Programs that do exist are often restricted to a specific specialty. We hypothesized that a 1-month elective for fourth-year medical students, composed of high-quality existing web-based resources and a project-based structure, would empower students to learn about the impact of AI and ML in their chosen specialty and begin contributing to innovation in their field of interest. This study aims to evaluate the success of this elective in improving self-reported confidence scores in AI and ML. The authors also share our curriculum with other educators who may be interested in its adoption. This elective was offered in 2 tracks: technical (for students who were already competent programmers) and nontechnical (with no technical prerequisites, focusing on building a conceptual understanding of AI and ML). Students established a conceptual foundation of knowledge using curated web-based resources and relevant research papers, and were then tasked with completing 3 projects in their chosen specialty: a data set analysis, a literature review, and an AI project proposal. The project-based nature of the elective was designed to be self-guided and flexible to each student's interest area and career goals. Students' success was measured by self-reported confidence in AI and ML skills in pre and postsurveys. Qualitative feedback on students' experiences was also collected. This web-based, self-directed elective was offered on a pass-or-fail basis each month to fourth-year students at Emory University School of Medicine beginning in May 2021. As of June 2022, a total of 19 students had successfully completed the elective, representing a wide range of chosen specialties: diagnostic radiology (n=3), general surgery (n=1), internal medicine (n=5), neurology (n=2), obstetrics and gynecology (n=1), ophthalmology (n=1), orthopedic surgery (n=1), otolaryngology (n=2), pathology (n=2), and pediatrics (n=1). Students' self-reported confidence scores for AI and ML rose by 66% after this 1-month elective. In qualitative surveys, students overwhelmingly reported enthusiasm and satisfaction with the course and commented that the self-direction and flexibility and the project-based design of the course were essential. Course participants were successful in diving deep into applications of AI in their widely-ranging specialties, produced substantial project deliverables, and generally reported satisfaction with their elective experience. The authors are hopeful that a brief, 1-month investment in AI and ML education during medical school will empower this next generation of physicians to pave the way for AI and ML innovation in health care.
Artificial Intelligence (AI) applications have the potential to revolutionize conventional healthcare practices, creating a more efficient and patient-centred approach with improved outcomes. This guide discuses eighteen AI-based applications in clinical decision-making, precision medicine, operational efficiency, and predictive analytics, including a real-world example of AI's role in public health during the early stages of the COVID-19 pandemic. Additionally, we address ethical questions, transparency, data privacy, bias, consent, accountability, and liability, and the strategic measures that must be taken to align AI with ethical principles, legal frameworks, legacy information technology systems, and employee skills and knowledge. We emphasize the importance of informed and strategic approaches to harness AI's potential and manage its challenges. Moreover, this guide underscores the importance of evaluating and integrating new skills and competencies to navigate and use AI-based technologies in healthcare management, such as technological literacy, long-term strategic vision, change management skills, ethical decision-making, and alignment with patient needs.
Artificial intelligence (AI) applications, specifically generative pre-trained transformers, have shown potential in medical education and board-style examinations. To assess this capability, we conducted a study comparing the performance of GPT-3.5 and GPT-4 on the American Urological Association (AUA) 2022 self-assessment study program (SASP) exams from 2012-2023. We used a standardized prompt to administer questions from the AUA SASP exams spanning 2012-2023, totalling 1679 questions. The performance of the two AI models, GPT-3.5 and GPT-4, was evaluated based on the number of questions answered correctly. Statistical analysis was performed using Fisher's exact test and independent sample t-tests to compare the performance of GPT-4 to that of GPT-3.5 among test years and urology topic areas. Percentile scores were not calculable, however, a score of 50% is required to acquire CME credits on AUA SASP exams. The analysis showed significantly superior performance by GPT-4, which scored above 50% across all exam years except 2018, with scores ranging from 48-64%. In contrast, GPT-3.5 consistently scored below this threshold, with scores ranging from 26-38%. The total combined score for GPT-4 was 55%, significantly higher than the 33% achieved by GPT-3.5 (odds ratio [OR] 2.5, 95% confidence interval [CI] 2.2-2.9, p<0.001). GPT-4 significantly outperformed GPT-3.5 among AUA SASP test years from 2012-2023 (mean difference 23, t(22) 14, 95% CI 19-26, p<0.001), as well as among urology topic areas (mean difference 21, t(52)=5.5, 95% CI 13-29, p<0.001). GPT-4 scored significantly higher than GPT-3.5 on the AUA SASP exams in overall performance, across all test years, and in various urology topic areas. This suggests improvement in evolving AI language models in answering clinical urology questions; however, certain aspects of medical knowledge and clinical reasoning remain challenging for AI language models.
Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to a readily accessible tool for cancer researchers. AI-based tools can boost research productivity in daily workflows, but can also extract hidden information from existing data, thereby enabling new scientific discoveries. Building a basic literacy in these tools is useful for every cancer researcher. Researchers with a traditional biological science focus can use AI-based tools through off-the-shelf software, whereas those who are more computationally inclined can develop their own AI-based software pipelines. In this article, we provide a practical guide for non-computational cancer researchers to understand how AI-based tools can benefit them. We convey general principles of AI for applications in image analysis, natural language processing and drug discovery. In addition, we give examples of how non-computational researchers can get started on the journey to productively use AI in their own work.
Artificial intelligence (AI) has birthed the new "big thing" in modern medicine. It promises to bring about safer and improved care that will be beneficial to patients and become a helpful tool in the hands of a skilled physician. Despite its anticipation, however, the implementation and usage of AI are still in their elementary phases, particularly due to legal and ethical considerations that border on "data." These challenges should not be brushed aside but rather be recognized and resolved to enable acceptance by all relevant stakeholders without prejudice. Once these challenges can be overcome, AI will truly revolutionize the field of medicine with improved diagnostic accuracy, a reduction in physician burnout, and an enhanced treatment modality. It is therefore paramount that AI be embraced by physicians and integrated into medical education in order to be well-prepared for our role in the future of medicine.
Artificial intelligence (AI) has the potential to revolutionize nursing education. This study compared NCLEX-RN questions generated by AI and those created by nurse educators. Faculty of accredited baccalaureate programs were invited to participate. Likert-scale items for grammar and clarity of the item stem and distractors were compared using Mann-Whitney U, and yes/no questions about clinical relevance and complex terminology were analyzed using chi-square. A one-sample binomial test with confidence intervals evaluated participants' question preference (AI-generated or educator-written). Qualitative responses identified themes across faculty. Item clarity, grammar, and difficulty were similar for AI and educator-created questions. Clinical relevance and use of complex terminology was similar for all question pairs. Of the four sets with preference for one item, three were generated by AI. AI can assist faculty with item generation to prepare nursing students for the NCLEX-RN examination. Faculty expertise is necessary to refine questions written using both methods. [J Nurs Educ. 2023;62(12):679-687. ].