Multifunctional Heterostructured Fe3O4-FeTe@MCM Electrocatalyst Enabling High-Performance Practical Lithium-Sulfur Batteries Via Built-in Electric Field.

The development of capable of simultaneously modulating the sluggish electrochemical kinetics, shuttle effect, and lithium dendrite growth is a promising strategy for the commercialization of lithium-sulfur batteries. Consequently, an elaborate preparation method is employed to create a host material consisting of multi-channel carbon microspheres (MCM) containing highly dispersed heterostructure Fe3O4-FeTe nanoparticles. The Fe3O4-FeTe@MCM exhibits a spontaneous built-in electric field (BIEF) and possesses both lithophilic and sulfophilic sites, rendering it an appropriate host material for both positive and negative electrodes. Experimental and theoretical results reveal that the existence of spontaneous BIEF leads to interfacial charge redistribution, resulting in moderate polysulfide adsorption which facilitates the transfer of polysulfides and diffusion of electrons at heterogeneous interfaces. Furthermore, the reduced conversion energy barriers enhanced the catalytic activity of Fe3O4-FeTe@MCM for expediting the bidirectional sulfur conversion. Moreover, regulated Li deposition behavior is realized because of its high conductivity and remarkable lithiophilicity. Consequently, the battery exhibited long-term stability for 500 cycles with 0.06% capacity decay per cycle at 5 C, and a large areal capacity of 7.3 mAh cm-2 (sulfur loading: 9.73 mg cm-2) at 0.1 C. This study provides a novel strategy for the rational fabrication of heterostructure hosts for practical Li-S batteries.

A Practical Significance Bias in Laypeople's Evaluation of Scientific Findings.

People often rely on scientific findings to help them make decisions-however, failing to report effect magnitudes might lead to a potential bias in assuming findings are practically significant. Across two online studies (Prolific; N = 800), we measured U.S. adults' endorsements of expensive interventions described in media reports that led to effects that were small, large, or of unreported magnitude between groups. Participants who viewed interventions with unreported effect magnitudes were more likely to endorse interventions compared with those who viewed interventions with small effects and were just as likely to endorse interventions as those who viewed interventions with large effects, suggesting a practical significance bias. When effect magnitudes were reported, participants on average adjusted their evaluations accordingly. However, some individuals, such as those with low numeracy skills, were more likely than others to act on small effects, even when explicitly prompted to first consider the meaningfulness of the effect.

Bayesian Mendelian randomization with an interval causal null hypothesis: ternary decision rules and loss function calibration.

We enhance the Bayesian Mendelian Randomization (MR) framework of Berzuini et al. (Biostatistics 21(1):86-101, 2018) by allowing for interval null causal hypotheses, where values of the causal effect parameter that fall within a user-specified interval of "practical equivalence" (ROPE) (Kruschke, Adv Methods Pract Psychol Sci 1(2):270-80, 2018) are regarded as equivalent to "no effect". We motivate this move in the context of MR analysis. In this approach, the decision over the hypothesis test is taken on the basis of the Bayesian posterior odds for the causal effect parameter falling within the ROPE. We allow the causal effect parameter to have a mixture prior, with components corresponding to the null and the alternative hypothesis. Inference is performed via Markov chain Monte Carlo (MCMC) methods. We speed up the calculations by fitting to the data a simpler model than the intended, "true", one. We recover a set of samples from the "true" posterior distribution by weighted importance resampling of the MCMC-generated samples. From the final samples we obtain a simulation consistent estimate of the desired posterior odds, and ultimately of the Bayes factor for the interval-valued null hypothesis, [Formula: see text], vs [Formula: see text]. In those situations where the posterior odds is neither large nor small enough, we allow for an uncertain outcome of the test decision, thereby moving to a ternary decision logic. Finally, we present an approach to calibration of the proposed method via loss function. We illustrate the method with the aid of a study of the causal effect of obesity on risk of juvenile myocardial infarction based on a unique prospective dataset.

A Review on Small Organic Colorimetric and Fluorescent Hosts for the Detection of Cobalt and Nickel Ion.

In recent years, there has been a notable increase in efforts to advance efficient hosts for detecting cobalt and nickel ions, driven by their extensive industrial applications and environmental significance. This review meticulously examines the progress made in small organic colorimetric and fluorescent hosts tailored specifically for the sensitive and selective detection of cobalt and nickel ions. It delves into a diverse range of molecular architectures, including organic ligands, elucidating their unique attributes such as sensitivity, selectivity, and response time. Moreover, the review precisely explores the underlying principles governing the colorimetric and fluorescent mechanisms employed by these hosts, shedding light on the intricate interactions between the sensing moieties and the target metal ions. Furthermore, it critically evaluates the practical applicability of these hosts, considering crucial factors such as detection limits, recyclability, and compatibility with complex sample matrices. Additionally, exploration extends to potential challenges and prospects in the field, emphasizing the imperative for ongoing innovation to address emerging environmental and analytical demands. Eventually, through this comprehensive examination, the review seeks to contribute to the ongoing endeavor to develop robust and efficient tools for monitoring and detecting cobalt and nickel metal ions in diverse analytical scenarios.

How to perform standardized sonographic examination of Cesarean scar pregnancy in the first trimester.

Early diagnosis and appropriate management of Cesarean scar pregnancy (CSP) are crucial to prevent severe complications such as uterine rupture, severe hemorrhage and placenta accreta spectrum disorders. In this article, we provide a step-by-step tutorial for the standardized sonographic evaluation of CSP in the first trimester. Practical steps for performing a standardized transvaginal ultrasound examination to diagnose CSP are outlined, focusing on criteria and techniques essential for accurate identification and classification. Key sonographic markers, including gestational sac location, cardiac activity, placental implantation and myometrial thickness, are detailed. The evaluation process is presented according to assessment of the uterine scar, differential diagnosis, detailed CSP evaluation and CSP classification. This step-by-step tutorial emphasizes the importance of scanning in two planes (sagittal and transverse), utilizing color or power Doppler and differentiating CSP from other low-lying pregnancies. The CSP classification is described in detail and is based on the location of the largest part of the gestational sac relative to the uterine cavity and serosal lines. This descriptive classification is recommended for clinical use to stimulate uniform description and evaluation. Such a standardized sonographic evaluation of CSP in the first trimester is essential for early diagnosis and management, helping to prevent life-threatening complications and to preserve fertility. Training sonographers in detailed evaluation techniques and promoting awareness of CSP are critical. The structured approach to CSP diagnosis presented herein is supported by a free e-learning course available online. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

State identification for a class of uncertain switched systems by differential neural networks.

This paper presents a non-parametric identification scheme for a class of uncertain switched nonlinear systems based on continuous-time neural networks. This scheme is based on a continuous neural network identifier. This adaptive identifier guaranteed the convergence of the identification errors to a small vicinity of the origin. The convergence of the identification error was determined by the Lyapunov theory supported by a practical stability variation for switched systems. The same stability analysis generated the learning laws that adjust the identifier structure. The upper bound of the convergence region was characterized in terms of uncertainties and noises affecting the switched system. A second finite-time convergence learning law was also developed to describe an alternative way of forcing the identification error's stability. The study presented in this paper described a formal technique for analysing the application of adaptive identifiers based on continuous neural networks for uncertain switched systems. The identifier was tested for two basic problems: a simple mechanical system and a switched representation of the human gait model. In both cases, accurate results for the identification problem were achieved.

Evolving Improved Sampling Protocols for Dose-Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric.

Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3-20), and it reduced the parameter variance metric by 33-37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.

A comparative analysis of computational drug repurposing approaches: proposing a novel tensor-matrix-tensor factorization method.

Efficient drug discovery relies on drug repurposing, an important and open research field. This work presents a novel factorization method and a practical comparison of different approaches for drug repurposing. First, we propose a novel tensor-matrix-tensor (TMT) formulation as a new data array method with a gradient-based factorization procedure. Additionally, this paper examines and contrasts four computational drug repurposing approaches-factorization-based methods, machine learning methods, deep learning methods, and graph neural networks-to fulfill the second purpose. We test the strategies on two datasets and assess each approach's performance, drawbacks, problems, and benefits based on results. The results demonstrate that deep learning techniques work better than other strategies and that their results might be more reliable. Ultimately, graph neural methods need to be in an inductive manner to have a reliable prediction.

Availability of genome-matched therapy based on clinical practice.

BACKGROUND: Comprehensive genomic profiling (CGP) provides new opportunities for patients with advanced cancer to receive genome-matched therapies, but the availability rate of these remains low. We reviewed our CGP cases and suggested possible strategies to improve the current status from a clinical perspective. METHODS: Druggable genomic alterations and barriers to accessing genome-matched therapies were investigated in 653 patients with 30 various types of cancers who underwent CGP. RESULTS: While the availability rate of genome-matched therapies as a whole was 9.5%, CGP was useful in some cancer types. Patients with thyroid cancer and lung cancer harbored druggable genomic alterations at high rates, while sarcoma rarely harbored these alterations (100%, 76%, and 15.2%, respectively). In contrast, the availability rate of genome-matched therapies was highest in patients with sarcoma and head and neck cancer (HNC) (60% and 40%, respectively). One hundred thirteen patients (63.5%) had multiple barriers to accessing genome-matched therapy. Of 178 patients, 21 patients (11.8%) could not be considered for genome-matched therapies solely because of the deterioration of their performance status. CONCLUSION: This study demonstrated the usefulness of CGP for patients with sarcoma and HNC in addition to lung cancer in clinical practice. Performing CGP at the front line has the potential to improve the availability of genome-matched therapy.

Construction and evaluation of a practical model for measuring health-adjusted life expectancy (HALE) in China.

BACKGROUND: HALE is now a regular strategic planning indicator for all levels of the Chinese government. However, HALE measurements necessitate comprehensive data collection and intricate technology. Therefore, effectively converting numerous diseases into the years lived with disability (YLD) rate is a significant challenge for HALE measurements. Our study aimed to construct a simple YLD rate measurement model with high applicability based on the current situation of actual data resources within China to address challenges in measuring HALE target values during planning. METHODS: First, based on the Chinese YLD rate in the Global Burden of Disease (GBD) 2019, Pearson correlation analysis, the global optimum method, etc., was utilized to screen the best predictor variables from the current Chinese data resources. Missing data for predictor variables were filled in via spline interpolation. Then, multiple linear regression models were fitted to construct the YLD rate measurement model. The Sullivan method was used to measure HALE. The Monte Carlo method was employed to generate 95% uncertainty intervals. Finally, model performances were assessed using the mean absolute error (MAE) and mean absolute percentage error (MAPE). RESULTS: A three-input-parameter model was constructed to measure the age-specific YLD rates by sex in China, directly using the incidence of infectious diseases, the incidence of chronic diseases among persons aged 15 and older, and the addition of an under-five mortality rate covariate. The total MAE and MAPE for the combined YLD rate were 0.0007 and 0.5949%, respectively. The MAE and MAPE of the combined HALE in the 0-year-old group were 0.0341 and 0.0526%, respectively. There were slightly fewer males (0.0197, 0.0311%) than females (0.0501, 0.0755%). CONCLUSION: We constructed a high-accuracy model to measure the YLD rate in China by using three monitoring indicators from the Chinese national routine as predictor variables. The model provides a realistic and feasible solution for measuring HALE at the national and especially regional levels, considering limited data.

Abolishing morality in biomedical ethics.

In biomedical ethics, there is widespread acceptance of moral realism, the view that moral claims express a proposition and that at least some of these propositions are true. Biomedical ethics is also in the business of attributing moral obligations, such as "S should do X." The problem, as we argue, is that against the background of moral realism, most of these attributions are erroneous or inaccurate. The typical obligation attribution issued by a biomedical ethicist fails to truly capture the person's actual obligations. We offer a novel argument for rife error in obligation attribution. The argument starts with the idea of an epistemic burden. Epistemic burdens are all of those epistemic obstacles one must surmount in order to achieve some aim. Epistemic burdens shape decision-making such that given two otherwise equal options, a person will choose the option that has the lesser of epistemic burdens. Epistemic burdens determine one's potential obligations and, conversely, their non-obligations. The problem for biomedical ethics is that ethicists have little to no access to others' epistemic burdens. Given this lack of access and the fact that epistemic burdens determine potential obligations, biomedical ethicists often can only attribute accurate obligations out of luck. This suggests that the practice of attributing obligations in biomedical ethics is rife with error. To resolve this widespread error, we argue that this practice should be abolished from the discourse of biomedical ethics.

Implementation of the Dementia Isolation Toolkit in long-term care improves awareness but does not reduce moral distress amongst healthcare providers.

BACKGROUND: Healthcare providers may experience moral distress when they are unable to take the ethically or morally appropriate action due to real or perceived constraints in delivering care, and this psychological stressor can negatively impact their mental health, leading to burnout and compassion fatigue. This study describes healthcare providers experiences of moral distress working in long-term care settings during the COVID-19 pandemic and measures self-reported levels of moral distress pre- and post-implementation of the Dementia Isolation Toolkit (DIT), a person-centred care intervention designed for use by healthcare providers to alleviate moral distress. METHODS: Subjective levels of moral distress amongst providers (e.g., managerial, administrative, and front-line employees) working in three long-term care homes was measured pre- and post-implementation of the DIT using the Moral Distress in Dementia Care Survey and semi-structured interviews. Interviews explored participants' experiences of moral distress in the workplace and the perceived impact of the intervention on moral distress. RESULTS: A total of 23 providers between the three long-term care homes participated. Following implementation of the DIT, subjective levels of moral distress measured by the survey did not change. When interviewed, participants reported frequent experiences of moral distress from implementing public health directives, staff shortages, and professional burnout that remained unchanged following implementation. However, in the post-implementation interviews, participants who used the DIT reported improved self-awareness of moral distress and reductions in the experience of moral distress. Participants related this to feeling that the quality of resident care was improved by integrating principals of person-centered care and information gathered from the DIT. CONCLUSIONS: This study highlights the prevalence and exacerbation of moral distress amongst providers during the pandemic and the myriad of systemic factors that contribute to experiences of moral distress in long-term care settings. We report divergent findings with no quantitative improvement in moral distress post-intervention, but evidence from interviews that the DIT may ease some sources of moral distress and improve the perceived quality of care delivered. This study demonstrates that an intervention to support person-centred isolation care in this setting had limited impact on overall moral distress during the COVID-19 pandemic.

Misconception between palliative care and euthanasia among Thai general practitioners: a cross-sectional study.

BACKGROUND: Palliative care lower medical expenses and enhances quality of life, but misconception with euthanasia delays timely care and makes inappropriate patient management. OBJECTIVE: To examine the magnitude of misconceptions between palliative care and euthanasia among Thai general practitioners, explore the association with knowledge, attitudes, and practical experience, and assess the association between misconception and confidence in practicing and referring patients to palliative care centers. METHODS: All 144 general practitioners who were going to start residency training at Maharaj Nakorn Chiang Mai Hospital in 2021 participated in this observational cross-sectional study. A chi-square test was utilized to examine the relationship between misconception and knowledge, attitude, practical experience, confidence to practice, and confidence to refer patients. Multivariable logistic regression was carried out while controlling for age, sex, knowledge, attitude, and experience to examine the relationship between misconception and confidence to practice and refer patients for palliative care. Statistical significance was defined at p < 0.05. RESULTS: About 41% of general physicians had misconceptions regarding palliative care and euthanasia. High knowledge was associated with a lower level of misconception (p = 0.01). The absence of misconceptions was weakly associated with a higher level of confidence in practicing palliative care, with an adjusted odds ratio of 1.51 (95% confidence interval 0.73 to 3.10, p = 0.07). CONCLUSION: High misconception rates between palliative care and euthanasia among young Thai physicians might impact their confidence in delivering palliative care. Training initiatives for medical students and practitioners can mitigate misconceptions, fostering better palliative care utilization in Thailand.

Investigating the Performance of Frequentist and Bayesian Techniques in Genomic Evaluation.

The genomic evaluation process relies on the assumption of linkage disequilibrium between dense single-nucleotide polymorphism (SNP) markers at the genome level and quantitative trait loci (QTL). The present study was conducted with the aim of evaluating four frequentist methods including Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, and Genomic Best Linear Unbiased Prediction (GBLUP) and five Bayesian methods including Bayes Ridge Regression (BRR), Bayes A, Bayesian LASSO, Bayes C, and Bayes B, in genomic selection using simulation data. The difference between prediction accuracy was assessed in pairs based on statistical significance (p-value) (i.e., t test and Mann-Whitney U test) and practical significance (Cohen's d effect size) For this purpose, the data were simulated based on two scenarios in different marker densities (4000 and 8000, in the whole genome). The simulated data included a genome with four chromosomes, 1 Morgan each, on which 100 randomly distributed QTL and two different densities of evenly distributed SNPs (1000 and 2000), at the heritability level of 0.4, was considered. For the frequentist methods except for GBLUP, the regularization parameter λ was calculated using a five-fold cross-validation approach. For both scenarios, among the frequentist methods, the highest prediction accuracy was observed by Ridge Regression and GBLUP. The lowest and the highest bias were shown by Ridge Regression and GBLUP, respectively. Also, among the Bayesian methods, Bayes B and BRR showed the highest and lowest prediction accuracy, respectively. The lowest bias in both scenarios was registered by Bayesian LASSO and the highest bias in the first and the second scenario were shown by BRR and Bayes B, respectively. Across all the studied methods in both scenarios, the highest and the lowest accuracy were shown by Bayes B and LASSO and Elastic Net, respectively. As expected, the greatest similarity in performance was observed between GBLUP and BRR ( d = 0.007 , in the first scenario and d = 0.003 , in the second scenario). The results obtained from parametric t and non-parametric Mann-Whitney U tests were similar. In the first and second scenario, out of 36 t test between the performance of the studied methods in each scenario, 14 ( P < . 001 ) and 2 ( P < . 05 ) comparisons were significant, respectively, which indicates that with the increase in the number of predictors, the difference in the performance of different methods decreases. This was proven based on the Cohen's d effect size, so that with the increase in the complexity of the model, the effect size was not seen as very large. The regularization parameters in frequentist methods should be optimized by cross-validation approach before using these methods in genomic evaluation.

Physicians' experiences and perceptions about withholding and withdrawal life-sustaining treatment in Chiang Mai University Hospital: a cross-sectional study.

BACKGROUND: Withholding or withdrawing life-sustaining treatment in end-of-life patients is a challenging ethical issue faced by physicians. Understanding physicians' experiences and factors influencing their decisions can lead to improvement in end-of-life care. OBJECTIVES: To investigate the experiences of Thai physicians when making decisions regarding the withholding or withdrawal of life-sustaining treatments in end-of-life situations. Additionally, the study aims to assess the consensus among physicians regarding the factors that influence these decisions and to explore the influence of families or surrogates on the decision-making process of physicians, utilizing case-based surveys. METHODS: A web-based survey was conducted among physicians practicing in Chiang Mai University Hospital (June - October 2022). RESULTS: Among 251 physicians (response rate 38.3%), most of the respondents (60.6%) reported that they experienced withholding or withdrawal treatment in end-of-life patients. Factors that influence their decision-making include patient's preferences (100%), prognosis (93.4%), patients' quality of life (92.8%), treatment burden (89.5%), and families' request (87.5%). For a chronic disease with comatose condition, the majority of the physicians (47%) chose to continue treatments, including cardiopulmonary resuscitation (CPR). In contrast, only 2 physicians (0.8%) would do everything, in cases when families or surrogates insisted on stopping the treatment. This increased to 78.1% if the families insisted on continuing treatment. CONCLUSION: Withholding and withdrawal of life-sustaining treatments are common in Thailand. The key factors influencing their decision-making process included patient's preferences and medical conditions and families' requests. Effective communication and early engagement in advanced care planning between physicians, patients, and families empower them to align treatment choices with personal values.

Changes in location and number of nurse consultations as the supply of general practitioners decreases in primary health care: six-year register-based follow-up cohort study in the city of Vantaa, Finland.

OBJECTIVE: To investigate whether the location and the number of nurse consultations have changed in response to the continuously decreasing number of GP consultations in the fourth-largest city in Finland. It has been suggested that nurse consultations are replacing GP consultations. DESIGN: A retrospective register-based follow-up cohort study. SETTING: Public primary health care in the City of Vantaa, Finland. SUBJECTS: All documented face-to-face office-hour consultations with practical and registered nurses, and consultations with practical and registered nurse in the emergency department of Vantaa primary health care between 1 January 2009 and 31 December, 2014. MAIN OUTCOME MEASURES: Change in the number of consultations with practical and registered nurses between 2009 and 2014 in primary health care both during office-hours and in the emergency department. RESULTS: Over the follow-up period, the monthly median number of practical nurse consultations in the emergency department per 1000 inhabitants increased from 1.6 (interquartile range [IQR] 1.3-1.7) to 10.5 (10.3-12.2) (p < 0.001) and registered nurse consultations from a median of 3.6 (3.0-4.0) to 14.5 (13.0-16.6) (p < 0.001). However, there was no significant change in the median monthly number of office-hour consultations with practical or registered nurses. CONCLUSIONS: It appears that in primary health care, medical consultations have shifted from GPs to nurses with lower education levels, and from care during office-hours to emergency care.

The number of general practitioner (GP) consultations are decreasing. Tasks are being transferred from GPs to nurses to improve access to care.The number of office-hour consultations with nurses did not change, despite the decrease in GP consultations.In the emergency department, the number of nurse consultations increased significantly when GP consultations decreased.Medical consultations seem to have shifted to the emergency department and the nurses.

Experiences of Electronic Health Records' and Client Information Systems' Use on a Mobile Device and Factors Associated With Work Time Savings Among Practical Nurses: Cross-Sectional Study.

BACKGROUND: The transmission of clinical information in nursing predominantly occurs through digital solutions, such as computers and mobile devices, in today's era. Various technological systems, including electronic health records (EHRs) and client information systems (CISs), can be seamlessly integrated with mobile devices. The use of mobile devices is anticipated to rise, particularly as long-term care is increasingly delivered in environments such as clients' homes, where computers are not readily accessible. However, there is a growing need for more user-centered data to ensure that mobile devices effectively support practical nurses in their daily activities. OBJECTIVE: This study aims to analyze practical nurses' experiences of using EHRs or CISs on a mobile device in their daily practice. In addition, it aims to examine the factors associated with work time savings when using EHRs/CISs on a mobile device. METHODS: A cross-sectional study using an electronic survey was conducted in spring 2022. A total of 3866 practical nurses participated in the survey based on self-assessment. The sample was limited to practical nurses who used EHRs or CISs on a mobile device and worked in home care or service housing within the social welfare or health care sector (n=1014). Logistic regression analysis was used to explore the factors associated with work time savings. RESULTS: The likelihood of perceiving work time savings was higher among more experienced EHR/CIS users compared with those with less experience (odds ratio [OR] 1.59, 95% CI 1.30-1.94). Participants with 0-5 years of work experience were more likely to experience work time savings compared with those who had worked 21 years or more (OR 2.41, 95% CI 1.43-4.07). Practical nurses in home care were also more likely to experience work time savings compared with those working in service housing (OR 1.95, 95% CI 1.23-3.07). A lower grade given for EHRs/CISs was associated with a reduced likelihood of experiencing work time savings (OR 0.76, 95% CI 0.66-0.89). Participants who documented client data in a public area were more likely to experience work time savings compared with those who did so in the nurses' office (OR 2.33, 95% CI 1.27-4.25). Practical nurses who found documentation of client data on a mobile device easy (OR 3.05, 95% CI 2.14-4.34) were more likely to experience work time savings compared with those who did not. Similarly, participants who believed that documentation of client data on a mobile device reduced the need to memorize things (OR 4.10, 95% CI 2.80-6.00) were more likely to experience work time savings compared with those who did not. CONCLUSIONS: To enhance the proportion of practical nurses experiencing work time savings, we recommend that organizations offer comprehensive orientation and regular education sessions tailored for mobile device users who have less experience using EHRs or CISs and find mobile devices less intuitive to use.

Extended Reality-New Opportunity for People With Disability? Practical and Ethical Considerations.

Since the introduction of virtual environments in the 70s, technologies have moved through virtual reality, mixed reality, and augmented reality into extended reality (XR). This development is promising for various groups. Previous research has shown people with disability benefiting from using technology in social and professional settings. Technology has offered people with disability the opportunity to communicate, interact, participate, and build new relationships. However, we do not know what impact XR has or will have and whether it will offer new opportunities for people with disability. This paper aims to indicate potential opportunities and challenges afforded by XR to people with disability. We offer reflections on the opportunities as well as the ethical considerations needed when introducing immersive technologies to a marginalized group.

Assessing Generative Pretrained Transformers (GPT) in Clinical Decision-Making: Comparative Analysis of GPT-3.5 and GPT-4.

BACKGROUND: Artificial intelligence, particularly chatbot systems, is becoming an instrumental tool in health care, aiding clinical decision-making and patient engagement. OBJECTIVE: This study aims to analyze the performance of ChatGPT-3.5 and ChatGPT-4 in addressing complex clinical and ethical dilemmas, and to illustrate their potential role in health care decision-making while comparing seniors' and residents' ratings, and specific question types. METHODS: A total of 4 specialized physicians formulated 176 real-world clinical questions. A total of 8 senior physicians and residents assessed responses from GPT-3.5 and GPT-4 on a 1-5 scale across 5 categories: accuracy, relevance, clarity, utility, and comprehensiveness. Evaluations were conducted within internal medicine, emergency medicine, and ethics. Comparisons were made globally, between seniors and residents, and across classifications. RESULTS: Both GPT models received high mean scores (4.4, SD 0.8 for GPT-4 and 4.1, SD 1.0 for GPT-3.5). GPT-4 outperformed GPT-3.5 across all rating dimensions, with seniors consistently rating responses higher than residents for both models. Specifically, seniors rated GPT-4 as more beneficial and complete (mean 4.6 vs 4.0 and 4.6 vs 4.1, respectively; P<.001), and GPT-3.5 similarly (mean 4.1 vs 3.7 and 3.9 vs 3.5, respectively; P<.001). Ethical queries received the highest ratings for both models, with mean scores reflecting consistency across accuracy and completeness criteria. Distinctions among question types were significant, particularly for the GPT-4 mean scores in completeness across emergency, internal, and ethical questions (4.2, SD 1.0; 4.3, SD 0.8; and 4.5, SD 0.7, respectively; P<.001), and for GPT-3.5's accuracy, beneficial, and completeness dimensions. CONCLUSIONS: ChatGPT's potential to assist physicians with medical issues is promising, with prospects to enhance diagnostics, treatments, and ethics. While integration into clinical workflows may be valuable, it must complement, not replace, human expertise. Continued research is essential to ensure safe and effective implementation in clinical environments.

Reliability and Validity Analyses of the Practical Assessment of Dysphagia Test in Stroke.

To investigate the validity and reliability of the Practical Assessment of Dysphagia (PAD) test as a quantitative and organ-specific test for stroke patients. In this study, PAD test data from 109 patients with stroke were used. The internal consistency of the PAD was analyzed using Cronbach's α value. Inter- and intra-rater reliabilities of the PAD were analyzed using Kappa coefficient. Concurrent validity was evaluated based on the correlation between PAD and the videofluoroscopic swallowing study (VFSS). The diagnostic accuracy of the PAD test in patients with stroke was measured using the area under the receiver operating characteristic (ROC) curve. Intra- and inter-rater reliabilities (Intra-class Correlation Coefficient (ICC) = 0.98 and 0.99, respectively) were significant (p < 0.001) for the total PAD score. The functional dysphagia scale (FDS) score and penetration-aspiration score (PAS) correlated significantly with PAD (p < 0.001). The results of the ROC curve analysis with various cut-off points showed that the PAD test had high sensitivity and specificity. The PAD has high reliability and validity. Therefore, it is a useful screening test for dysphagia in patients with stroke.