Yoxas/statistical_literacy huggingface dataset:
Exercise has a profound effect on insulin sensitivity in skeletal muscle. The euglycaemic-hyperinsulinaemic clamp (EHC) is the gold standard for assessment of insulin sensitivity but it does not reflect the hyperglycaemia that occurs after eating a meal. In previous EHC investigations, it has been shown that the interstitial glucose concentration in muscle is decreased to a larger extent in previously exercised muscle than in rested muscle. This suggests that previously exercised muscle may increase its glucose uptake more than rested muscle if glucose supply is increased by hyperglycaemia. Therefore, we hypothesised that the exercise-induced increase in muscle insulin sensitivity would appear greater after eating a meal than previously observed with the EHC. Ten recreationally active men performed dynamic one-legged knee extensor exercise for 1 h. Following this, both femoral veins and one femoral artery were cannulated. Subsequently, 4 h after exercise, a solid meal followed by two liquid meals were ingested over 1 h and glucose uptake in the two legs was measured for 3 h. Muscle biopsies from both legs were obtained before the meal test and 90 min after the meal test was initiated. Data obtained in previous studies using the EHC (n=106 participants from 13 EHC studies) were used for comparison with the meal-test data obtained in this study. Plasma glucose and insulin peaked 45 min after initiation of the meal test. Following the meal test, leg glucose uptake and glucose clearance increased twice as much in the exercised leg than in the rested leg; this difference is twice as big as that observed in previous investigations using EHCs. Glucose uptake in the rested leg plateaued after 15 min, alongside elevated muscle glucose 6-phosphate levels, suggestive of compromised muscle glucose metabolism. In contrast, glucose uptake in the exercised leg plateaued 45 min after initiation of the meal test and there were no signs of compromised glucose metabolism. Phosphorylation of the TBC1 domain family member 4 (TBC1D4; p-TBC1D4Ser704) and glycogen synthase activity were greater in the exercised leg compared with the rested leg. Muscle interstitial glucose concentration increased with ingestion of meals, although it was 16% lower in the exercised leg than in the rested leg. Hyperglycaemia after meal ingestion results in larger differences in muscle glucose uptake between rested and exercised muscle than previously observed during EHCs. These findings indicate that the ability of exercise to increase insulin-stimulated muscle glucose uptake is even greater when evaluated with a meal test than has previously been shown with EHCs.
Exercise physiology courses have transitioned to competency based, forcing Universities to rethink assessment to ensure students are competent to practice. This study built on earlier research to explore rater cognition, capturing factors that contribute to assessor decision making about students' competency. The aims were to determine the source of variation in the examination process and document the factors impacting on examiner judgment. Examiner judgement was explored from both a quantitative and qualitative perspective. Twenty-three examiners viewed three video encounters of student performance on an OSCE. Once rated, analysis of variance was performed to determine where the variance was attributed. A semi-structured interview drew out the examiners reasoning behind their ratings. Results highlighted variability of the process of observation, judgement and rating, with each examiner viewing student performance from different lenses. However, at a global level, analysis of variance indicated that the examiner had a minimal impact on the variance, with the majority of variance explained by the student performance on task. One anomaly noted was in the assessment of technical competency, whereby the examiner had a large impact on the rating, linked to assessing according to curriculum content. The thought processes behind judgement were diverse and if the qualitative results had been used in isolation, may have led to the researchers drawing conclusions that the examined performances would have yielded widely different ratings. However, as a cohort, the examiners were able to distinguish good and poor levels of competency with the majority of student competency linked to the varying ability of the student.
Existing health informatics curriculum requirements mostly use a competency-based approach rather than a skill-based one. The main objective of this study was to assess the current skills training requirements in graduate health informatics curricula to evaluate graduate students' confidence in specific health informatics skills. A quantitative cross-sectional observational study was developed to evaluate published health informatics curriculum requirements and to determine the comprehensive health informatics skill sets required in a research university in New York, United States. In addition, a questionnaire to assess students' confidence about specific health informatics skills was developed and sent to all enrolled and graduated Master of Science students in a health informatics program. The evaluation was performed in a graduate health informatics program, and analysis of the students' self-assessments questionnaire showed that 79.4% (81/102) of participants were not confident (not at all confident or slightly confident) about developing an artificial intelligence app, 58.8% (60/102) were not confident about designing and developing databases, and 54.9% (56/102) were not confident about evaluating privacy and security infrastructure. Less than one-third of students (24/105, 23.5%) were confident (extremely confident and very confident) that they could evaluate the use of data capture technologies and develop mobile health informatics apps (10/102, 9.8%). Health informatics programs should consider specialized tracks that include specific skills to meet the complex health care delivery and market demand, and specific training components should be defined for different specialties. There is a need to determine new competencies and skill sets that promote inductive and deductive reasoning from diverse and various data platforms and to develop a comprehensive curriculum framework for health informatics skills training.
Existing literature does not provide accurate epidemiological data regarding the true prevalence of men with non-obstructive azoospermia (NOA) who would be eligible for hormonal optimization therapy, according to specific pre-treatment criteria. To investigate the characteristics of those men with NOA who would qualify for the medical therapy prior to any SR procedure in a large multi-centric cross-sectional study. Complete data from 1644 NOA patients seeking medical help for primary infertility at three tertiary referral centers from USA, Brazil, and Italy were analyzed. Baseline serum hormone levels were collected for all patients. NOA was confirmed after two consecutive semen analyses. Genetic tests, including karyotype analysis and Y microdeletions, were performed on all patients. Patients with secondary hypogonadism (total testosterone (T) levels less than 300 ng/dL and luteinizing hormone (LH) levels less than 8 mIU/mL) were earmarked as potential candidates for receiving clomiphene citrate (CC) and/or human chorionic gonadotropin (hCG). Patients with a T to 17β-estradiol (E2) ratio < 10 were classified as eligible for aromatase inhibitors (AIs) therapy (e.g., anastrazole). A third sub-cohort was created by combining the criteria of the first two sub-cohorts. Descriptive statistics was used to detail overall characteristics and differences between the different sub-cohorts. Among the 1,644 men, 28% (n = 460) had T < 300 ng/dL and LH < 8 mIU/mL, thereby being potentially suitable for CC and/or hCG, while 37% (n = 607) had a T to E2 ratio < 10 thus potentially suitable for AIs. Lastly, 17.7% (n = 280) met the criteria for potential eligibility for both CC and/or hCG and AIs. Findings from this multicentric cross-sectional study reveal that about 30% of men with NOA were eligible for hormonal treatment with CC and/or hCG while 37% were found to be potential candidates for AIs, and 17% for both therapies. Therefore, these findings show that a only a small subset of NOA patients can benefit from medical therapy prior to considering any SR procedures.
Existing methods in social [oral] epidemiology primarily utilize statistical models that assume static characteristics of individuals and environments. While useful, an over reliance on these methods in the social and behavioural sciences can unnecessarily limit perspective and progress as even the most advanced statistical methods cannot capture complex behaviour over time given that systems evolve, environments respond, and behaviours and beliefs crystalize or deteriorate based on a variety of social, environmental and access variables. The recent consensus statement on Future Directions for the Behavioral and Social Sciences in Oral Health acknowledges that dental, oral and craniofacial health emerge from the complex interplay of multiple factors at multiple levels over time and highlights the need for the incorporation of new and underutilized methodologies. Complex Systems Science offers a suite of tools and methodologies that are responsive to the generative mechanisms and processes that underlie population distributions of oral health and disease. Specifically, they assume intricate, dynamic interactions between individuals and groups, they facilitate the study and synthesis of interconnections between people (e.g. patients, healthcare providers and policy makers), how these change over time, any differences across settings, and provide an opportunity to guide future longitudinal data collection and intervention science more effectively. This paper aims to provide an introduction to foundational principles of complex systems, complex systems thinking, and methods found in complex systems science, including social network analysis, system dynamics models and agent-based models, and offers perspectives on the challenges faced and opportunities afforded in the incorporation of these methods into the population oral health sciences.
Existing natural language processing (NLP) methods to convert free-text clinical notes into structured data often require problem-specific annotations and model training. This study aims to evaluate ChatGPT's capacity to extract information from free-text medical notes efficiently and comprehensively. We developed a large language model (LLM)-based workflow, utilizing systems engineering methodology and spiral "prompt engineering" process, leveraging OpenAI's API for batch querying ChatGPT. We evaluated the effectiveness of this method using a dataset of more than 1000 lung cancer pathology reports and a dataset of 191 pediatric osteosarcoma pathology reports, comparing the ChatGPT-3.5 (gpt-3.5-turbo-16k) outputs with expert-curated structured data. ChatGPT-3.5 demonstrated the ability to extract pathological classifications with an overall accuracy of 89%, in lung cancer dataset, outperforming the performance of two traditional NLP methods. The performance is influenced by the design of the instructive prompt. Our case analysis shows that most misclassifications were due to the lack of highly specialized pathology terminology, and erroneous interpretation of TNM staging rules. Reproducibility shows the relatively stable performance of ChatGPT-3.5 over time. In pediatric osteosarcoma dataset, ChatGPT-3.5 accurately classified both grades and margin status with accuracy of 98.6% and 100% respectively. Our study shows the feasibility of using ChatGPT to process large volumes of clinical notes for structured information extraction without requiring extensive task-specific human annotation and model training. The results underscore the potential role of LLMs in transforming unstructured healthcare data into structured formats, thereby supporting research and aiding clinical decision-making.
Existing research has investigated student problem-solving strategies across science, technology, engineering, and mathematics; however, there is limited work in undergraduate biology education on how various aspects that influence learning combine to generate holistic approaches to problem solving. Through the lens of situated cognition, we consider problem solving as a learning phenomenon that involves the interactions between internal cognition of the learner and the external learning environment. Using phenomenography as a methodology, we investigated undergraduate student approaches to problem solving in biology through interviews. We identified five aspects of problem solving (including knowledge, strategy, intention, metacognition, and mindset) that define three qualitatively different approaches to problem solving; each approach is distinguishable by variations across the aspects. Variations in the knowledge and strategy aspects largely aligned with previous work on how the use or avoidance of biological knowledge informed both concept-based and nonconcept-based strategies. Variations in the other aspects revealed intentions spanning complete disengagement to deep interest with the course material, different degrees of metacognitive reflections, and a continuum of fixed to growth mindsets. We discuss implications for how these characterizations can improve instruction and efforts to support development of problem-solving skills.
Existing research has shown that intimate partner violence (IPV) may hinder maternal access to healthcare services, thereby affecting maternal and child health. However, current studies have ignored whether emotional intimate partner violence (EV) could negatively affect maternal healthcare use. This study aims to evaluate the impact of invisible IPV on maternal healthcare utilization in Pakistan. We analyzed nationally representative data from the Pakistan Demographic and Health Survey database from 2012-2013 and 2017-2018. Exposure to physical intimate partner violence (PV) and EV was the primary predictor. Based on women's last birth records, outcomes included three binary variables indicating whether women had inadequate antenatal care (ANC) visits, non-institutional delivery, and lack of postnatal health check-ups. A logistic regression model was established on weighted samples. Exposure to EV during pregnancy was significantly associated with having inadequate ANC visits (aOR = 2.16, 95% CI: 1.06 to 4.38, p = 0.033) and non-institutional delivery (aOR = 2.24, 95% CI: 1.41 to 3.57, p = 0.001). Lifetime exposure to EV was associated with increased risks of inadequate ANC visits (aOR = 1.48, 95% CI: 1.00 to 2.19, p = 0.049). Lifetime exposure to low-scale physical intimate partner violence (LSPV) (adjusted OR (aOR) = 1.73, 95% CI: 1.29 to 2.31, p < 0.001) was associated with increased risks of having no postnatal health check-ups. Pregnant women who experienced EV and LSPV are at greater risk of missing maternal healthcare, even if the violence occurred before pregnancy. Therefore, in countries with high levels of IPV, early screening for invisible violence needs to be integrated into policy development, and healthcare providers need to be trained to identify EV and LSPV.
Existing studies focusing on "smart" guns, weapons that can only be fired by an authorized user, have focused largely on overall views towards the topic. Little is known about how views differ within key demographics or why Americans feel positively or negatively towards smart guns. This study used data from a nationwide web survey of 520 college students to address these concerns. Multinomial regression was used to identify patterns of smart gun preference in quantitative data. Respondents' open-ended reasoning for their views was examined through common themes and descriptive statistics. About half of the sample preferred smart guns over traditional firearms, with support more likely among females and liberals. Those with more advanced academic standing were more likely to have a traditional gun preference. The sample did not rank cost as a significant concern. Child protection was a much less prominent theme than concerns over (un)authorized users.
Existing studies on disease diagnostic models focus either on diagnostic model learning for performance improvement or on the visual explanation of a trained diagnostic model. We propose a novel learn-explain-reinforce (LEAR) framework that unifies diagnostic model learning, visual explanation generation (explanation unit), and trained diagnostic model reinforcement (reinforcement unit) guided by the visual explanation. For the visual explanation, we generate a counterfactual map that transforms an input sample to be identified as an intended target label. For example, a counterfactual map can localize hypothetical abnormalities within a normal brain image that may cause it to be diagnosed with Alzheimer's disease (AD). We believe that the generated counterfactual maps represent data-driven knowledge about a target task, i.e., AD diagnosis using structural MRI, which can be a vital source of information to reinforce the generalization of the trained diagnostic model. To this end, we devise an attention-based feature refinement module with the guidance of the counterfactual maps. The explanation and reinforcement units are reciprocal and can be operated iteratively. Our proposed approach was validated via qualitative and quantitative analysis on the ADNI dataset. Its comprehensibility and fidelity were demonstrated through ablation studies and comparisons with existing methods.
Expanded access programs (EAPs) allow cancer patients with unmet clinical need to obtain access to pre-authorisation treatments. There is no standardised process for implementing these programs nationally, and real-world data on their impact is lacking. This study aimed to evaluate the prevalence of such EAPs and their impact in a cancer centre. Data relating to adult cancer patients treated via EAPs from 2011 to 2021 in three Cork university hospitals was collated. Descriptive statistics were employed to get an overview of the impact these programs currently have on cancer care provision. We identified 193 patients who accessed EAPs during the study period, availing of 33 separate drugs for a total of 50 different cancer indications. The prevalence of EAP usage was shown to have been trending upwards in recent years with a total of 189 programs being accessed throughout the period. Drugs provided were from a number of different anti-cancer drug classes, particularly targeted therapies (n = 18) and immune checkpoint inhibitors (n = 17). Cancers from a wide range of both solid and liquid tumour types were treated with EAP drugs, and patients treated were from across a broad spectrum of ages (26-82, SD 11.99). EAPs have an increasing role in accessing novel cancer therapies in our community and by extension nationally. Equity of EAP access would be facilitated by a national registry of available agents which we have established. Assessment of their benefits and toxicities would be enhanced by the requirement for a real-world database as a condition of EAP approval.
Expedited partner therapy prescription remains low and highly variable throughout the United States, leading to frequent reinfections with Chlamydia trachomatis and Neisseria gonorrhoeae . We examined provider counseling on expedited partner therapy before and after an electronic smart tool-based initiative. In this quasi-experimental interrupted time-series study, we implemented an initiative of electronic smart tools and education for expedited partner therapy in March 2020. We reviewed the records of patients with chlamydia and/or gonorrhea at an urban, academic obstetrics and gynecology clinic in the preimplementation (March 2019-February 2020) and postimplementation (March 2020-February 2021) groups. Descriptive statistics and an interrupted time-series model were used to compare the percent of expedited partner therapy offered by clinicians to patients in each group. A total of 287 patient encounters were analyzed, 155 preintervention and 132 postintervention. An increase in expedited partner therapy counseling of 13% (95% confidence interval [CI], 2%-24%) was observed before the intervention (27.1% [42 of 155]) versus after the intervention (40.2% [53 of 132]). Significant increases in provider counseling were seen for patients who were single (15%; 95% CI, 3%-26%), 25 years or older (21%; 95% CI, 6%-37%), receiving public insurance (15%; 95% CI, 3%-27%), seen by a registered nurse (18%; 95% CI, 4%-32%), or seen for an obstetrics indication (21%; 95% CI, 4%-39%). No difference was seen in patients' acceptance of expedited partner therapy ( P = 1.00). A multicomponent initiative focused on electronic smart tools is effective at increasing provider counseling on expedited partner therapy. Further research to understand patient perceptions and acceptance of expedited partner therapy is critical.
Experiences regarding the use of simulation in low-resource settings like Uganda where it has not taken root have not been explored. The purpose of the study was to explore the experiences of students, clinical staff, and faculty regarding the use of clinical simulation in teaching undergraduate students. The study was conducted at Busitema and Lira Universities in Uganda. We conducted 20 in-depth interviews with the faculty staff and 10 focused group discussions with undergraduate Nursing, Midwifery, Medical and Anesthesia students. The study obtained ethical clearance from the Busitema University Research and Ethics Committee (BUFHS-2023-78) and Uganda National Council of Science and Technology (HS3027ES). Thematic analysis was used to analyze the data. Four themes emerged from the data. Simulation was seen to be about improvising and (return) demonstration. Concerns of realism were expressed including notions that simulation was not real, that simulation felt real and the extreme end that simulation tends to present the ideal setting. Perceived benefits of simulation include room for mistakes and immediate feedback, enhanced confidence and self-efficacy, enhanced acquisition of soft and clinical skills, prepares students for clinical placement, convenient and accessible. Concerns were expressed related to whether skills in clinical simulation would translate to clinical competence in the clinical setting. Students perceived simulation to be beneficial. However, concerns about realism and transferability of skills to clinical settings were noted. Clarifying preconceived notions against the use of clinical simulation will enhance its utilization in educational settings where simulation is not readily embraced.