Yoxas/statistical_literacy huggingface dataset:
The lack of harmonization of evaluation criteria by Ethics Committees in the European Union (EU) has led to inconsistent ethics reviews received by research sites participating in multicenter non-interventional studies. The European General Data Protection Regulation (GDPR) appears to be implemented at national level with a substantial degree of variance in interpretation. The European Reference Networks (ERNs) were struggling in setting an Informed Consent Form (ICF) for registries, allowing reuse of data for research purposes. The aim of this work is to develop an adaptable ICF for research purposes to be used in ERN registries. To work on this challenge, a team was established within the European Joint Programme on Rare Diseases (EJP RD) to develop a patients' registry ICF template allowing easy adaptation to ERNs, country, and site-level specificities. ERN and patients' representatives validated the choice of developing a GDPR-compliant template for research purposes. The feedback received from 34 Ethics Committees on the Clinical Patient Management System ICF, including the submission of patients' data to the ERN registries and the EU consent regulatory framework were analyzed along with existing ontologies for data access and reuse. An adaptable ICF was developed following iterative cycles of consultation and review by clinicians, research experts, ethics and regulatory advisors, and patients' representatives. The development of pediatric material for minor participants was also undertaken. Research oriented ICF templates for adults and for parents/legal representatives of patients were released in 26 national languages. This adaptable ICF aims to foster, according to patients' preferences, the reuse of registries data for research purposes in compliance with the applicable laws and standards. Pediatric material is being finalized to collect minors' assent. ICF machine-readability is also progressing to enhance data discovery and facilitate its access and reuse conditions.
The lack of interpretability in artificial intelligence models (i.e., deep learning, machine learning, and rules-based) is an obstacle to their widespread adoption in the healthcare domain. The absence of understandability and transparency frequently leads to (i) inadequate accountability and (ii) a consequent reduction in the quality of the predictive results of the models. On the other hand, the existence of interpretability in the predictions of AI models will facilitate the understanding and trust of the clinicians in these complex models. The data protection regulations worldwide emphasize the relevance of the plausibility and verifiability of AI models' predictions. In response and to take a role in tackling this challenge, we designed the interpretability-based model with algorithms that achieve human-like reasoning abilities through statistical analysis of the datasets by calculating the relative weights of the variables of the features from the medical images and the patient symptoms. The relative weights represented the importance of the variables in predictive decision-making. In addition, the relative weights were used to find the positive and negative probabilities of having the disease, which indicated high fidelity explanations. Hence, the primary goal of our model is to shed light and give insights into the prediction process of the models, as well as to explain how the model predictions have resulted. Consequently, our model contributes by demonstrating accuracy. Furthermore, two experiments on COVID-19 datasets demonstrated the effectiveness and interpretability of the new model.
The lack of work appeared to be linked to several symptoms related to poor mental health. Likewise, the reverse relationship, namely the influence of poor mental health on the risk of job loss, has also been analysed, i.e. distress could lead to a poorer work performance culminating in potential job loss. Thus, the bidirectional nature of the relationship between unemployment and mental health makes the accurate estimation of causal relationships a complex matter, leaving room for additional research on the subject. The aim of this research is to analyse the influence that unemployment could have on mental health taking into account the bidirectional nature that exists between both concepts. In order to tackle the causal effect of being unemployed on mental health, we present a biprobit model taking into account the presence of dummy endogenous regressors and we compare these results with those obtained from a standard univariate probit. Our identification strategy exploits geographical information on the unemployment rates as instrument. We use Spanish cross-sectional data from the 2006, 2011 and 2017 years. Based on the results, the paper concludes that unemployed persons in Spain could be subject to a 5.4% higher probability of suffering symptoms related to a common mental disorder (versus 11% obtained using a standard probit). The results obtained confirm a negative impact of unemployment situation on mental health. In other words, the probability of unemployed people suffering a mental disorder seems superior to that for individuals with a job. Moreover, the marginal effect obtained from a univariate probit model without the possibility of controlling the mental health selection effects, proves the existence of a problem of simultaneity that would have overestimated the effect of being unemployed on mental health. It is hoped that the conclusions obtained here prove useful in the implementation of specific mental health care provision aimed at unemployed people. In this context, the evidence obtained should result in the incorporation of health assistance as an essential part in response to the needs of this collective. These special needs of unemployed people should be contemplated not only from a health care provision but also as part of a broader system that incorporates the mental health care of unemployed persons as part of more general public health policies. Finally, these results suggest that mental health-related objectives should be considered when planning, implementing, and evaluating active labour market policies for the unemployed. The length and severity of the last recession, together with the risks associated with the global crisis resulting from COVID-19, reiterate the obvious concerns about the consequences of economic crises and unemployment on personal mental health. In this context, our study could be a step forward in the study of the causal relationships between unemployment and mental health when new data are available.
The landscape of otolaryngology training in Saudi Arabia is undergoing transformation due to the expansion of medical colleges and increased overseas medical scholarships. However, concerns persist regarding the satisfaction and adequacy of surgical education. This study aims to assess gaps in otolaryngology training through an in-depth needs assessment. A cross-sectional study was conducted among 85 otolaryngology-head and neck surgery residency graduates in Saudi Arabia between 2019 and 2021. Participants completed a validated questionnaire assessing deficiencies, importance, and competence in different subspecialty areas. Data were analyzed using descriptive statistics, median comparisons, and Kruskal-Wallis tests. Participants identified deficiencies in training across domains, with significant variations in specific subspecialties among different regions. Dissatisfaction with clinical discussions, research training, access to simulation labs, and training in emerging subspecialties was evident. The study highlights challenges within otolaryngology training, emphasizing the need for continuous evaluation and adaptation to ensure high-quality and comprehensive training. Addressing these gaps is essential to produce well-rounded otolaryngologists capable of meeting the evolving demands of modern healthcare.
The Language of Thought (LoT) hypothesis proposes that some collections of mental states and processes are symbol systems to explain language-like systematic properties of thought. Recent proponents of this hypothesis point to additional LoT-like properties in non-linguistic domains to claim that LoT remains the "best game in town" in terms of explanatory coverage. Nonetheless, LoT assumes but does not explain why/how symbolic representations connect to other (non-symbolic) formats. The perspective presented here is supposed to bridge this gap as a duality in a category theoretical sense: (perceptual) data are projected onto a base (conceptual) space in one direction, and in the opposite direction, these data are referenced by that space. Accordingly, perception is dual to conception. These constructions follow from a universal mapping principle affording an explanation for why/how symbolic and non-symbolic formats are connected: as the "best" possible transformation between the two forms- so the slogan, LoT is universal. This view also sheds some light on the apparent pervasiveness of logic-like capacities across age-groups and species, and these constructions constitute special types of categories called toposes (topoi), and every topos has an interpretation in first-order logic.
The Laparoscopic Approach to Cervical Cancer (LACC) trial was published in 2018 and demonstrated that minimally invasive surgery (MIS) yields inferior survival outcomes in early-stage cervical cancer compared to open surgery. This study investigates how the results of the LACC trial have impacted the selection of the primary treatment modality and adjuvant radiation utilization in early-stage cervical cancer. Using the National Cancer Database (NCDB), we compared patients with stage IA2-IB1 cervical cancer before (1/2016-12/2017) and after (1/2019-12/2020) the LACC trial. A total of 7930 patients were included: 4609 before and 3321 after the LACC trial. There was a decline in MIS usage from 67% pre-LACC to 35% thereafter (p < 0.001). In both the pre- and post-LACC periods, patients undergoing radical MIS more frequently had small volume disease (pre-LACC tumors ≤ 2 cm, 48% MIS vs. 41% open, p = 0.023; post-LACC stage IA2, 22% vs. 15%, p = 0.002). Pre-LACC, MIS radical hysterectomy was associated with White race (82% vs. 77%, p = 0.001) and private insurance (63% vs. 54%, p = 0.004), while there was no difference in socioeconomic factors in the post-LACC period. Although the proportion of patients treated with primary chemoradiation remained stable, the post-LACC cohort had a younger median age (52.47 vs. 56.37, p = 0.005) and more microscopic disease cases (13% vs. 5.4%, p = 0.002). There was no difference in the rate of radiation after radical hysterectomy before and after the trial (26% vs. 24%, p = 0.3). Conclusions: Post-LACC, patients were less likely to undergo MIS but received adjuvant radiation at similar rates, and primary chemoradiation patients were younger and more likely to have microscopic disease.
The last 15 years have seen an explosion of measurement tools for assessing the development of young children in low- and middle- income countries. This paper builds on and contributes to that literature by identifying a core set of caregiver-report items and a core set of direct assessment items that measure key developmental domains for children aged 4-6 (48-83 months) and that demonstrate adequate psychometric properties across diverse contexts, the first in this age group to the authors' knowledge. Data were harmonized from previous early childhood measurement efforts in 12 countries that all used the same base measurement tool. Data analyses yielded 20 caregiver report items and 84 child direct assessment items (grouped into 16 tasks) that show strong item-level statistics across countries and that cover the domains of early literacy, early numeracy, executive functioning, and social-emotional competencies. Next steps include adding data and items from other measurement tools to the same analytical framework and field testing across a number of contexts and early childhood measurement efforts. The vision is for the resulting core sets of items, along with guidance on data collection, management, and analysis, to serve as global public goods so that they can (i) present a starting point for linking across different early childhood measurement tools for children aged 4-6; (ii) increase quality across measurement efforts; and (iii) facilitate the scale up of early childhood measurement. When supplemented with items that capture local contexts and their measurement needs, these core sets of items should help to advance understanding of universal and context-specific factors that underlie child development and thus help policymakers make decisions that ensure children receive the quality early childhood care and education they need in order to reach their full potential.
The last decade has witnessed unprecedented growth in mobile phone use. It links millions of previously unconnected people. The ubiquity of mobile phones, which allows for use of the short message service (SMS), offers new and innovative opportunities for disease prevention and health education. SMS usage appears to be a feasible, popular, and effective way of improving health literacy. This study measured the effect of SMS health education on the improvement of health management in Shenzhen, China. This was a community-based randomized controlled study. A total of 32 communities were randomly chosen out of 320, then about 200 participants were randomly sampled from each selected community. The subjects were equally divided into two groups at random. About half of the participants received health intervention messages via Internet-based SMS for almost a year. The data were analyzed by descriptive and inferential statistical methods. The proportion of participants involved in self-health management increased from 30.92% to 38.68% over the year (χ2 = 42.49, p < 0.001) in the intervention group. People with marginal health literacy reported the highest increase (10.92%), while people with low health literacy reported the smallest (5.25%). The control group showed no difference in baseline and final health management proportions (28.02% and 29.64%, p > 0.05). No statistical difference in the prevalence of chronic disease (15.16% and 13.89%, p > 0.05) was found before and after the intervention in the intervention group. The prevalence in the intervention group was lower after the intervention than it was in the control group (17.33%, χ2 = 14.45, p < 0.001). SMS may be a powerful tool for improving the public's health literacy and health management because it is widely available, popular, affordable, and instant.
The last national estimates of US intensive care unit (ICU) physician staffing are 25 years old and lack information about interprofessional teams. How are US adult ICUs currently staffed? We conducted a cross-sectional survey (05/04/2022-02/02/2023) of adult ICU clinicians (targeting nurse/physician leadership) contacted using 2020 American Hospital Association (AHA) database information and, secondarily, through professional organizations. The survey included questions about interprofessional ICU staffing availability and roles at steady-state (pre-COVID-19). We linked survey data to hospital data in the AHA database to create weighted national estimates by extrapolating ICU staffing data to non-respondent hospitals based on hospital characteristics. The cohort consisted of 596 adult ICUs (response rates-AHA contacts: 2.1%; professional organizations: unknown) with geographic diversity and size variability (median [interquartile range]: 20 [12,25] beds); most cared for mixed populations (414 [69.5%]), yet medical (55 [9.2%]), surgical (70 [11.7%]), and specialty (57 [9.6%]) ICUs were well represented. 554 (93.0%) had intensivists available, with intensivists covering all patients in 75.6% of these and onsite 24 hours/day in half (53.3% weekdays; 51.8% weekends). Of all ICUs, 69.8% had physicians-in-training and 77.7% nurse practitioners/physician assistants. For mechanically ventilated patients, nurse:patient ratios were 1:2 in 89.6%. Clinical pharmacists were available in 92.6%, and respiratory therapists in 98.8%. We estimated 85.1% (95% confidence interval: 85.7%, 84.5%) of hospitals nationally had ICUs with intensivists, 51.6% (50.6%, 52.5%) with physicians-in-training, 72.1% (71.3%, 72.9%) with nurse practitioners/physician assistants, 98.5% (98.4%, 98.7%) with respiratory therapists, and 86.9% (86.4%, 87.4%) with clinical pharmacists. For mechanically ventilated patients, 86.4% (85.8%, 87.0%) used 1:2 nurses:patients. Intensivist presence in adult US ICUs has greatly increased over 25 years. Intensivists, respiratory therapists, and clinical pharmacists are commonly available, and each nurse usually provides care for two mechanically ventilated patients. However, team composition and workload vary.
The latent structure model and association rules analysis were employed to explore the compatibility rules of prescriptions for heart failure of dilated cardiomyopathy, with a view to providing theoretical support for the clinical treatment of this disease based on syndrome differentiation and the formulation of guidelines. The articles about the treatment of heart failure of dilated cardiomyopathy were retrieved from CNKI, Wanfang, VIP, and SinoMed. The database was established in Microsoft Excel 2019. Lantern 5.0 and Rstudio were used to analyze the latent structure and association rules of Chinese medicine with the frequency greater than 4.00%. Furthermore, the frequency structure model was used to mine the rules of prescriptions for heart failure of dilated cardiomyopathy. The study included 175 traditional Chinese medicine(TCM) prescriptions, involving 128 Chinese medicines, with the cumulative frequency of 1 847. High-frequency medicines included Astragali Radix, Salviae Miltiorrhizae Radix et Rhizoma, Poria, Cinnamomi Ramulus, Glycyrrhizae Radix et Rhizoma, with the main effects of tonifying, activating blood, resolving stasis, and releasing exterior. A total of 17 hidden variables, 34 hidden categories, and 6 comprehensive cluster models, along with 15 core prescriptions, were obtained. According to the prescriptions, the patients mainly had the syndromes of heart-Yang and Qi deficiency, Qi deficiency and blood stasis, heart-kidney Yang deficiency or Qi-Yin deficiency. Fifty-four strong association rules were obtained through association rule analysis. The highest degree of support was observed for the combination of Salviae Miltiorrhizae Radix et Rhizoma-Astragali Radix, while the highest degree of confidence was found for the combination of Salviae Miltiorrhizae Radix et Rhizoma-Cinnamomi Ramulus-Ophiopogonis Radix-Astragali Radix. The heart failure of dilated cardiomyopathy, characterized by internal deficiency and excess manifestations, is attributed to deficiency, stasis, and water. These factors are closely associated with the heart, lung, and spleen. The treatment should follow the principle of invigorating Qi and warming Yang, and meanwhile the method of activating blood and resolve stasis or moving Qi and promoting urination can be adopted according to the specific syndrome of patients.
The latest iteration of GPT4 (generative pretrained transformer) is a large multimodal model that can integrate both text and image input, but its performance with medical images has not been systematically evaluated. We studied whether ChatGPT with GPT-4V(ision) can recognize images from common nuclear medicine examinations and interpret them. Fifteen representative images (scintigraphy, 11; PET, 4) were submitted to ChatGPT with GPT-4V(ision), both in its Default and "Advanced Data Analysis (beta)" version. ChatGPT was asked to name the type of examination and tracer, explain the findings and whether there are abnormalities. ChatGPT should also mark anatomical structures or pathological findings. The appropriateness of the responses was rated by 3 nuclear medicine physicians. The Default version identified the examination and the tracer correctly in the majority of the 15 cases (60% or 53%) and gave an "appropriate" description of the findings or abnormalities in 47% or 33% of cases, respectively. The Default version cannot manipulate images. "Advanced Data Analysis (beta)" failed in all tasks in >90% of cases. A "major" or "incompatible" inconsistency between 3 trials of the same prompt was observed in 73% (Default version) or 87% of cases ("Advanced Data Analysis (beta)" version). Although GPT-4V(ision) demonstrates preliminary capabilities in analyzing nuclear medicine images, it exhibits significant limitations, particularly in its reliability (ie, correctness, predictability, and consistency).
The launch of the eagle-i Consortium, a collaborative network for sharing information about research resources, such as protocols and reagents, provides a vivid demonstration of the challenges that researchers, libraries and institutions face in making their data available to others.
The layout, scale and spatial form of urban employment centers are important guidelines for the rational layout of public service facilities such as urban transportation, medical care, and education. In this paper, we use Internet cell phone positioning data to identify the workplace and residence of users in the Beijing city area and obtain commuting data of the employed to measure the employment center system in Beijing. Firstly, the employment density distribution is generated using the data of the working places of the employed persons, and the employment centers are identified based on the employment density of Beijing. Then, we use the business registration data of employment centers to measure the industrial diversity within the employment centers by using the ecological Shannon Wiener Diversity Index, and combine the commuting links between employment centers and places of residence to measure the energy level of each employment center, analyze the hinterland and sphere of influence of each center, and finally using the industrial diversity index of employment centers and the average commuting time of employed persons, combined with the K-Means clustering algorithm, to classify the employment centers in Beijing. The employment center identification and classification method based on big data constructed in this study can help solve the limitations of the previous employment center system research in terms of center identification and commuting linkage measurement due to large spatial units and lack of commuting data to a certain extent. The study can provide reference for the regular understanding and technical analysis of employment centers and provide help for the employment multi-center system in Beijing in terms of quantifying the employment spatial structure, guiding the construction of multi-center system, and adjusting the land use rules.