¿Influye la comunicación con el paciente sobre la kinesiofobia? Una revisión sistemática / Does patient communication influence kinesiophobia? A systematic review

El objetivo de la presente revisión sistemática consistió en determinar el efecto de la comunicación en el ámbito sanitario sobre la kinesiofobia. Para ello, se realizó una búsqueda bibliográfica en siete bases de datos entre noviembre de 2022 y febrero de 2023. La revisión se efectuó acorde a la declaración Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) y para el análisis de la calidad metodológica se utilizaron: la escala Physiotherapy Evidence Database (PEDro), los criterios de Van Tulder y el análisis del riesgo de sesgo de la Colaboración Cochrane. Se incluyeron un total de 13 artículos que presentaron una calidad metodológica media de 7,1 sobre 10. Se obtuvieron resultados significativos para al menos una variable (kinesiofobia, discapacidad o nivel de actividad física) en 12 trabajos. Existe evidencia sólida de que la comunicación puede influir sobre la kinesiofobia del sujeto. Es más probable que esta influencia ocurra en un sentido negativo o discapacitante, pero también puede actuar en sentido positivo disminuyendo la misma. (AU)

The aim of the present systematic review was to determine the effect of communication in the health care setting on kinesiophobia. To this end, a literature search was conducted in seven databases between November 2022 and February 2023. The review was carried out following the PRISMA statement and for the analysis of methodological quality we used: PEDro Scale, Van Tulder criteria and risk of bias analysis of the Cochrane Collaboration. A total of 13 articles were included with a mean methodological quality of 7.1 out of 10. Significant results were obtained for at least one variable (kinesiophobia, disability or level of physical activity) in 12 articles. There is strong evidence that communication can influence a subject's kinesiophobia. This influence is most likely to be in a negative or disabling sense, but it can also act in a positive sense by decreasing it. (AU)

Predicting hotspots of unsheltered homelessness using geospatial administrative data and volunteered geographic information.

Unsheltered homelessness is an increasingly prevalent phenomenon in major cities that is associated with adverse health and mortality outcomes. This creates a need for spatial estimates of population denominators for resource allocation and epidemiological studies. Gaps in the timeliness, coverage, and spatial specificity of official Point-in-Time Counts of unsheltered homelessness suggest a role for geospatial data from alternative sources to provide interim, neighborhood-level estimates of counts and trends. We use citizen-generated data from homeless-related 311 requests, provider-based administrative data from homeless street outreach cases, and expert reports of unsheltered count to predict count and emerging hotspots of unsheltered homelessness in census tracts across the City of Los Angeles for 2019 and 2020. Our study shows that alternative data sources can contribute timely insights into the state of unsheltered homelessness throughout the year and inform the delivery of interventions to this vulnerable population.

Microplastics in seafood: Consumer preferences and valuation for mitigation technologies.

Microplastics, an emerging pollutant, have garnered widespread attention due to potential repercussions on human health and the environment. Given the critical role of seafood in food security, growing concerns about microplastics might be detrimental to meeting future global food demand. This study employed a discrete choice experiment to investigate Chilean consumers' preferences for technology aimed at mitigating microplastic levels in mussels. Using a between-subjects design with information treatments, we examined the impact of informing consumers about potential human health and environmental effects linked to microplastics pollution on their valuation for the technology. We found that the information treatments increased consumers' willingness to pay for mussels. Specifically, consumers were willing to pay a premium of around US$ 4 for 250 g of mussel meat with a 90 % depuration efficiency certification. The provision of health impact information increased the price premium by 56 %, while the provision of environmental information increased it by 21 %. Furthermore, combined health and environmental information significantly increased the probability of non-purchasing behavior by 22.8 % and the risk perception of microplastics for human health by 5.8 %. These results emphasized the critical role of information in shaping consumer preferences and provided evidence for validating investment in research and development related to microplastic pollution mitigation measures.

Stroke awareness and knowledge in Sudan: a cross-sectional analysis of public perceptions and understanding.

Introduction: Stroke, a leading cause of morbidity and mortality globally, demands heightened awareness and knowledge for effective preventive strategies and tailored response. Sudan is classified as a low income country with a low rate of literacy, lack of knowledge, and awareness about diseases. Thus, this study aimed to assess stroke awareness and knowledge among Sudanese population, and identify the associated factors influencing awareness. Methods: A cross-sectional study conducted between October and November 2022 through a self-administered online survey distributed via various social media platforms. The study involved adults aged 18 years and above through snow-ball sampling technique. The survey covered general awareness and knowledge concerning stroke risk factors, consequences, and the appropriate responses taken during acute stroke attacks. Results: A total of 410 participants were enrolled in the study, majority (93.4%) were from urban area and had university degree (92.4%). Furthermore, 92.2% were aware about stroke and 74.9% were able to recognize the symptoms of stroke. Only 40.2% identified all correct answers, 96.3, 92.3, and 95.1% recognized at least one risk factor, early symptom, and consequences, respectively. Females were significantly more than males able to identify at least one risk factor. Almost all participants (99.5%) perceived stroke as a serious disease (99.5%). Notably, 86.3% would promptly transport a suspected stroke patient to the hospital. The multivariable analysis showed that females versus males and patients with depression versus without depression had significantly higher odds to identify at least one risk factor (OR of 14.716 [95% CI 1.901; 113.908] and 0.241 [95% CI 0.059; 0.984], respectively). Conclusion: The study concluded that stroke knowledge and awareness among Sudanese population is suboptimal. Furthermore, early stroke recognition and intake of the appropriate management strategies are lacking which highlights the need for targeted education and awareness campaigns.

Buffering or boosting? The dynamic curvilinear relationship between work-related use of information and communication technologies after-hours and wok procrastination.

Work-related use of information and communication technologies after-hours (W_ICTs) blurs the boundaries between work and non-work domains, representing a typical boundary-crossing behavior that affects employees' lives and organizational development. Drawing on the Job Demands-Resources theory, this study develops a dynamic curvilinear model of the impact of W_ICTs on work procrastination, considering intrinsic motivation (self-efficacy and enjoyment) and regulatory focus (prevention focus). Empirical testing of the research hypotheses is conducted through a survey involving 817 employees with standard working hours (e.g. 9 a.m. to 5 p.m.). The results indicate that W_ICTs can be regarded as both inhibitors and promoters, with a U-shaped impact on work procrastination and an inverted U-shaped effect on self-efficacy and enjoyment. The mediating roles of self-efficacy and enjoyment are significant. Moreover, prevention focus moderates the relationship between W_ICTs and enjoyment, whereas the moderating effect between W_ICTs and self-efficacy is insignificant. This dynamic curvilinear relationship may explain the inconsistent results of prior studies regarding the relationship between W_ICTs and employees' negative behaviors. It contributes to expanding research on the outcomes of W_ICTs and the antecedents of work procrastination. Moreover, the proposed influence mechanism between W_ICTs and work procrastination has not been established from the perspective of intrinsic motivation and prevention focus. Hence, this study responds to scholars' calls and adds to the existing research on how W_ICTs affect work procrastination. These research findings enhance the current understanding of the effects of W_ICTs and offer valuable insights for organizations to effectively manage W_ICTs and address work procrastination behavior in practice.

Method for rock fracture prediction and early warning: Insight from fusion of multi-physics field information.

Understanding the precursors leading to rock fracture is crucial for ensuring safety in mining and geotechnical engineering projects. To effectively discern these precursors, a collaborative monitoring approach that integrates multiple sources of information is imperative. This paper considered a rock multi-parameter monitoring loading system, incorporating infrared radiation and acoustic emission monitoring technologies to simultaneously track the rock fracture process. The study delves into the spatiotemporal evolution patterns of infrared radiation and acoustic emission in rock under loading. Utilizing stress, cumulative acoustic emission count, and average infrared radiation temperature (AIRT), the paper establishes a comprehensive evaluation model termed "acoustic-thermal-stress" fusion information, employing principal component analysis (PCA). The research reveals that the sensitivity to rock sample damage response follows the sequence of cumulative acoustic emission count, AIRT, and stress. Furthermore, a novel method for identifying rock fracture precursors is proposed, based on the first derivative of the comprehensive evaluation model. This method addresses the limitations of single physical field information, enhancing the robustness of monitoring data. It determines the average stress level of fracture precursors to be 0.77σmax. Subsequently, the study defines the probability function of rock damage during loading and fracture, enabling the realization of probability-based warnings for rock fracture. This approach introduces a new perspective on rock fracture prediction, significantly contributing to safety monitoring and warning systems in mine safety and geotechnical engineering. The findings of this research hold paramount engineering significance, offering valuable insights for enhancing safety measures in such projects.

Risks and benefits of sharing patient information on social media: a digital dilemma.

Social media (SoMe) has witnessed remarkable growth and emerged as a dominant method of communication worldwide. Platforms such as Facebook, X (formerly Twitter), LinkedIn, Instagram, TikTok, and YouTube have become important tools of the digital native generation. In the field of medicine, particularly, cardiology, attitudes towards SoMe have shifted, and professionals increasingly utilize it to share scientific findings, network with experts, and enhance teaching and learning. Notably, SoMe is being leveraged for teaching purposes, including the sharing of challenging and intriguing cases. However, sharing patient data, including photos or images, online carries significant implications and risks, potentially compromising individual privacy both online and offline. Privacy and data protection are fundamental rights within European Union treaties, and the General Data Protection Regulation (GDPR) serves as the cornerstone of data protection legislation. The GDPR outlines crucial requirements, such as obtaining 'consent' and implementing 'anonymization', that must be met before sharing sensitive and patient-identifiable information. Additionally, it is vital to consider the patient's perspective and prioritize ethical and social considerations when addressing challenges associated with sharing patient information on SoMe platforms. Given the absence of a peer-review process and clear guidelines, we present an initial approach, a code of conduct, and recommendations for the ethical use of SoMe. In conclusion, this comprehensive review underscores the importance of a balanced approach that ensures patient privacy and upholds ethical standards while harnessing the immense potential of SoMe to advance cardiology practice and facilitate knowledge dissemination.

Low frequency signal detection via correlated Ramsey measurements.

The low frequency region of the spectrum is a challenging regime for quantum probes. We support the idea that, in this regime, performing Ramsey measurements carefully controlling the time at which each measurement is initiated is an excellent signal detection strategy. We use the Fisher information to demonstrate a high quality performance in the low frequency regime, compared to more elaborated measurement sequences, and to optimize the correlated Ramsey sequence according to any given experimental parameters, showing that correlated Ramsey rivals with state-of-the-art protocols, and can even outperform commonly employed sequences such as dynamical decoupling in the detection of low frequency signals. Contrary to typical quantum detection protocols for oscillating signals, which require adjusting the time separation between pulses to match the half period of the target signal, and consequently see their scope limited to signals whose period is shorter than the characteristic decoherence time of the probe, or to those protocols whose target is primarily static signals, the time-tagged correlated Ramsey sequence simultaneously tracks the amplitude and the phase information of the target signal, regardless of its frequency, which crucially permits correlating measurements in post-processing, leading to efficient spectral reconstruction.

Drastic reduction in the notification of acute cases of Chagas disease in the Northeast region of Brazil. Epidemiological evaluation in the period 2001-2021.

Chagas disease (CD), caused by the protozoan Trypanosoma cruzi (T. cruzi), is a neglected disease endemic to some Latin American countries, including Brazil. Soon after infection, individuals develop an acute phase, which in most cases is asymptomatic and may go undetected. However, when CD is detected early, notification in the Notifiable Diseases Information System (SINAN), is mandatory. This study aimed to evaluate the information registered in the SINAN database and to determine the epidemiological profile of acute CD in Northeast Brazil, an endemic region, from 2001 to 2021. According to this survey, 1,444 cases of acute CD were reported in the Northeastern region of Brazil during this period. During the first six years, referred to as period 1, 90.24% of the notifications were registered, while the number of notifications significantly decreased in the subsequent years, referred to as period 2. Most individuals diagnosed with acute CD were Afro-Brazilian adults. All known routes of infection by the parasite were reported. Vector-borne transmission was predominant during period 1 (73.29%) and oral transmission during period 2 (58.87%). All nine states in Northeast Brazil reported cases in both periods. A higher incidence of disease was reported in Rio Grande do Norte (RN) during period 1, and in Maranhão (MA) during period 2. Our results show that CD remains a significant public health challenge.

Mixed messages? Exposure to reports about alcohol's suggested cardiovascular effects and hazardous alcohol use: a cross-sectional study of patients in cardiology care.

BACKGROUND: Hazardous alcohol use is a leading risk factor for disability and death, yet observational studies have also reported reduced cardiovascular disease mortality among regular, low-level drinkers. Such findings are refuted by more recent research, yet have received significant media coverage. We aimed to explore: (1) how patients with cardiovascular diseases access health information about moderate drinking and cardiovascular health; (2) the perceived messages these sources convey, and (3) associations with own level of alcohol use. METHODS: We conducted a cross-sectional survey of patients in cardiology services at three hospitals in Sweden. The study outcome was hazardous alcohol use, assessed using the AUDIT-C questionnaire and defined as ≥ 3 in women and ≥ 4 in men. The exposure was accessing information sources suggesting that moderate alcohol consumption can be good for the heart, as opposed to accessing information that alcohol is bad for the heart. Health information sources were described using descriptive statistics. Gender, age and education were adjusted for in multiple logistic regression analyses. RESULTS: A total of 330 (66.3%) of 498 patients (mean age 70.5 years, 65% males) who had heard that drinking moderately can affect the heart described being exposed to reports that moderate alcohol use can be good for the heart, and 108 (21.7%) met criteria for hazardous alcohol use. Health information sources included newspapers (32.9%), television (29.2%), healthcare staff (13.4%), friends/family (11.8%), social media (8.9%) and websites (3.7%). Participants indicated that most reports (77.9%) conveyed mixed messages about the cardiovascular effects of moderate drinking. Exposure to reports of healthy heart effects, or mixed messages about the cardiovascular effects of alcohol, was associated with increased odds of hazardous alcohol use (OR = 1.67, 95%CI = 1.02-2.74). CONCLUSIONS: This study suggests that many patients in cardiology care access health information about alcohol from media sources, which convey mixed messages about the cardiovascular effects of alcohol. Exposure to reports that moderate drinking has protective cardiovascular effects, or mixed messages about the cardiovascular effects of alcohol, was associated with increased odds of hazardous alcohol use. Findings highlight a need for clear and consistent messages about the health effects of alcohol.

Difference in Rumor Dissemination and Debunking Before and After the Relaxation of COVID-19 Prevention and Control Measures in China: Infodemiology Study.

BACKGROUND: The information epidemic emerged along with the COVID-19 pandemic. While controlling the spread of COVID-19, the secondary harm of epidemic rumors to social order cannot be ignored. OBJECTIVE: The objective of this paper was to understand the characteristics of rumor dissemination before and after the pandemic and the corresponding rumor management and debunking mechanisms. This study aimed to provide a theoretical basis and effective methods for relevant departments to establish a sound mechanism for managing network rumors related to public health emergencies such as COVID-19. METHODS: This study collected data sets of epidemic rumors before and after the relaxation of the epidemic prevention and control measures, focusing on large-scale network rumors. Starting from 3 dimensions of rumor content construction, rumor propagation, and rumor-refuting response, the epidemic rumors were subdivided into 7 categories, namely, involved subjects, communication content, emotional expression, communication channels, communication forms, rumor-refuting subjects, and verification sources. Based on this framework, content coding and statistical analysis of epidemic rumors were carried out. RESULTS: The study found that the rumor information was primarily directed at a clear target audience. The main themes of rumor dissemination were related to the public's immediate interests in the COVID-19 field, with significant differences in emotional expression and mostly negative emotions. Rumors mostly spread through social media interactions, community dissemination, and circle dissemination, with text content as the main form, but they lack factual evidence. The preferences of debunking subjects showed differences, and the frequent occurrence of rumors reflected the unsmooth channels of debunking. The χ2 test of data before and after the pandemic showed that the P value was less than .05, indicating that the difference in rumor content before and after the pandemic had statistical significance. CONCLUSIONS: This study's results showed that the themes of rumors during the pandemic are closely related to the immediate interests of the public, and the emotions of the public accelerate the spread of these rumors, which are mostly disseminated through social networks. Therefore, to more effectively prevent and control the spread of rumors during the pandemic and to enhance the capability to respond to public health crises, relevant authorities should strengthen communication with the public, conduct emotional risk assessments, and establish a joint mechanism for debunking rumors.

Attention and sensory integration for gait in young adults with autism spectrum disorder.

INTRODUCTION: Altered sensorimotor function is a common feature of autism spectrum disorder (ASD). As a result, spatiotemporal walking patterns are typically affected. Attentional processes relevant for locomotion may be altered in people with ASD. This study assessed the extent to which gait alterations observed under sensory challenging conditions are due to reduced attention-related processes in young adults with ASD. METHODS: Twenty-one adults with ASD and 21 age- and sex-matched neurotypical participants walked at a self-selected pace on a 10-m walkway under 12 sensory/attention conditions: hard or carpet flooring; well-lit or dim lighting; no attention task, an auditory choice-reaction time information-processing task, or a simple reaction time information-processing task. Gait data were collected with a 12-marker motion capture set and a trunk accelerometer. Spatiotemporal characteristics of gait were derived and compared between the two groups across gait conditions. RESULTS: Floor/light conditions impacted gait speed, average step length, average stance time, average step width, and step width variability similarly in both groups (p<0.05). The information processing tasks impacted average step length, gait speed, and step length variability (p<0.05). Group differences were found in step length metrics: the ASD group had decreased average step length during the simple reaction time information-processing task and neurotypical participants did not (p=0.039); the ASD group had increased variability on carpet compared to hard floor and the neurotypical group had no change in variability due to floor (p=0.015). SIGNIFICANCE: These results suggest that attentional set-shifting and somatosensory inputs may play an important role in ASD-related gait alterations. Step length metrics appear to be sensitive to group differences between ASD and neurotypical adults during sensory challenging conditions.

Enhancing Readability of Online Patient-Facing Content: The Role of AI Chatbots in Improving Cancer Information Accessibility.

BACKGROUND: Internet-based health education is increasingly vital in patient care. However, the readability of online information often exceeds the average reading level of the US population, limiting accessibility and comprehension. This study investigates the use of chatbot artificial intelligence to improve the readability of cancer-related patient-facing content. METHODS: We used ChatGPT 4.0 to rewrite content about breast, colon, lung, prostate, and pancreas cancer across 34 websites associated with NCCN Member Institutions. Readability was analyzed using Fry Readability Score, Flesch-Kincaid Grade Level, Gunning Fog Index, and Simple Measure of Gobbledygook. The primary outcome was the mean readability score for the original and artificial intelligence (AI)-generated content. As secondary outcomes, we assessed the accuracy, similarity, and quality using F1 scores, cosine similarity scores, and section 2 of the DISCERN instrument, respectively. RESULTS: The mean readability level across the 34 websites was equivalent to a university freshman level (grade 13±1.5). However, after ChatGPT's intervention, the AI-generated outputs had a mean readability score equivalent to a high school freshman education level (grade 9±0.8). The overall F1 score for the rewritten content was 0.87, the precision score was 0.934, and the recall score was 0.814. Compared with their original counterparts, the AI-rewritten content had a cosine similarity score of 0.915 (95% CI, 0.908-0.922). The improved readability was attributed to simpler words and shorter sentences. The mean DISCERN score of the random sample of AI-generated content was equivalent to "good" (28.5±5), with no significant differences compared with their original counterparts. CONCLUSIONS: Our study demonstrates the potential of AI chatbots to improve the readability of patient-facing content while maintaining content quality. The decrease in requisite literacy after AI revision emphasizes the potential of this technology to reduce health care disparities caused by a mismatch between educational resources available to a patient and their health literacy.

Information needs of caregivers of adults with intellectual and/or developmental disabilities in India.

Many families of adults with intellectual and/or developmental disabilities in India experience difficulty in accessing services/supports, due to lack of awareness/knowledge of disability rights/laws and available services, and in accessing the services. There remains insufficient research on the information needs of these caregivers and on designing interventions that aim to increase their awareness/knowledge about human rights and supports/services. A strengths-based mixed methods needs assessment was conducted to understand the information needs of these family caregivers. Results showed that caregivers ≥50 years had significantly higher information needs than younger caregivers. Specifically, caregivers with no proficiency in English needed more information on the available services for the care recipients (n = 100). Qualitative results showed that very few caregivers had any awareness or access to information on human rights, disability-related laws/policies or available supports/services (n = 15). Study findings underscore the government's role in improving awareness-raising initiatives and imparting the information in multiple Indian languages.

Connecting process models to response times through Bayesian hierarchical regression analysis.

Process models specify a series of mental operations necessary to complete a task. We demonstrate how to use process models to analyze response-time data and obtain parameter estimates that have a clear psychological interpretation. A prerequisite for our analysis is a process model that generates a count of elementary information processing steps (EIP steps) for each trial of an experiment. We can estimate the duration of an EIP step by assuming that every EIP step is of random duration, modeled as draws from a gamma distribution. A natural effect of summing several random EIP steps is that the expected spread of the overall response time increases with a higher EIP step count. With modern probabilistic programming tools, it becomes relatively easy to fit Bayesian hierarchical models to data and thus estimate the duration of a step for each individual participant. We present two examples in this paper: The first example is children's performance on simple addition tasks, where the response time is often well predicted by the smaller of the two addends. The second example is response times in a Sudoku task. Here, the process model contains some random decisions and the EIP step count thus becomes latent. We show how our EIP regression model can be extended to such a case. We believe this approach can be used to bridge the gap between classical cognitive modeling and statistical inference and will be easily applicable to many use cases.

Houston Methodist cardiovascular learning health system (CVD-LHS) registry: Methods for development and implementation of an automated electronic medical record-based registry using an informatics framework approach.

Objectives: To investigate the potential value and feasibility of creating a listing system-wide registry of patients with at-risk and established Atherosclerotic Cardiovascular Disease (ASCVD) within a large healthcare system using automated data extraction methods to systematically identify burden, determinants, and the spectrum of at-risk patients to inform population health management. Additionally, the Houston Methodist Cardiovascular Disease Learning Health System (HM CVD-LHS) registry intends to create high-quality data-driven analytical insights to assess, track, and promote cardiovascular research and care. Methods: We conducted a retrospective multi-center, cohort analysis of adult patients who were seen in the outpatient settings of a large healthcare system between June 2016 - December 2022 to create an EMR-based registry. A common framework was developed to automatically extract clinical data from the EMR and then integrate it with the social determinants of health information retrieved from external sources. Microsoft's SQL Server Management Studio was used for creating multiple Extract-Transform-Load scripts and stored procedures for collecting, cleaning, storing, monitoring, reviewing, auto-updating, validating, and reporting the data based on the registry goals. Results: A real-time, programmatically deidentified, auto-updated EMR-based HM CVD-LHS registry was developed with ∼450 variables stored in multiple tables each containing information related to patient's demographics, encounters, diagnoses, vitals, labs, medication use, and comorbidities. Out of 1,171,768 adult individuals in the registry, 113,022 (9.6%) ASCVD patients were identified between June 2016 and December 2022 (mean age was 69.2 ± 12.2 years, with 55% Men and 15% Black individuals). Further, multi-level groupings of patients with laboratory test results and medication use have been analyzed for evaluating the outcomes of interest. Conclusions: HM CVD-LHS registry database was developed successfully providing the listing registry of patients with established ASCVD and those at risk. This approach empowers knowledge inference and provides support for efforts to move away from manual patient chart abstraction by suggesting that a common registry framework with a concurrent design of data collection tools and reporting rapidly extracting useful structured clinical data from EMRs for creating patient or specialty population registries.

Exploring effects of anesthesia on complexity, differentiation, and integrated information in rat EEG.

To investigate mechanisms underlying loss of consciousness, it is important to extend methods established in humans to rodents as well. Perturbational complexity index (PCI) is a promising metric of "capacity for consciousness" and is based on a perturbational approach that allows inferring a system's capacity for causal integration and differentiation of information. These properties have been proposed as necessary for conscious systems. Measures based on spontaneous electroencephalography recordings, however, may be more practical for certain clinical purposes and may better reflect ongoing dynamics. Here, we compare PCI (using electrical stimulation for perturbing cortical activity) to several spontaneous electroencephalography-based measures of signal diversity and integrated information in rats undergoing propofol, sevoflurane, and ketamine anesthesia. We find that, along with PCI, the spontaneous electroencephalography-based measures, Lempel-Ziv complexity (LZ) and geometric integrated information (ΦG), were best able to distinguish between awake and propofol and sevoflurane anesthesia. However, PCI was anti-correlated with spontaneous measures of integrated information, which generally increased during propofol and sevoflurane anesthesia, contrary to expectations. Together with an observed divergence in network properties estimated from directed functional connectivity (current results) and effective connectivity (earlier results), the perturbation-based results seem to suggest that anesthesia disrupts global cortico-cortical information transfer, whereas spontaneous activity suggests the opposite. We speculate that these seemingly diverging results may be because of suppressed encoding specificity of information or driving subcortical projections from, e.g., the thalamus. We conclude that certain perturbation-based measures (PCI) and spontaneous measures (LZ and ΦG) may be complementary and mutually informative when studying altered states of consciousness.

Utility of Large Language Models for Health Care Professionals and Patients in Navigating Hematopoietic Stem Cell Transplantation: Comparison of the Performance of ChatGPT-3.5, ChatGPT-4, and Bard.

BACKGROUND: Artificial intelligence is increasingly being applied to many workflows. Large language models (LLMs) are publicly accessible platforms trained to understand, interact with, and produce human-readable text; their ability to deliver relevant and reliable information is also of particular interest for the health care providers and the patients. Hematopoietic stem cell transplantation (HSCT) is a complex medical field requiring extensive knowledge, background, and training to practice successfully and can be challenging for the nonspecialist audience to comprehend. OBJECTIVE: We aimed to test the applicability of 3 prominent LLMs, namely ChatGPT-3.5 (OpenAI), ChatGPT-4 (OpenAI), and Bard (Google AI), in guiding nonspecialist health care professionals and advising patients seeking information regarding HSCT. METHODS: We submitted 72 open-ended HSCT-related questions of variable difficulty to the LLMs and rated their responses based on consistency-defined as replicability of the response-response veracity, language comprehensibility, specificity to the topic, and the presence of hallucinations. We then rechallenged the 2 best performing chatbots by resubmitting the most difficult questions and prompting to respond as if communicating with either a health care professional or a patient and to provide verifiable sources of information. Responses were then rerated with the additional criterion of language appropriateness, defined as language adaptation for the intended audience. RESULTS: ChatGPT-4 outperformed both ChatGPT-3.5 and Bard in terms of response consistency (66/72, 92%; 54/72, 75%; and 63/69, 91%, respectively; P=.007), response veracity (58/66, 88%; 40/54, 74%; and 16/63, 25%, respectively; P<.001), and specificity to the topic (60/66, 91%; 43/54, 80%; and 27/63, 43%, respectively; P<.001). Both ChatGPT-4 and ChatGPT-3.5 outperformed Bard in terms of language comprehensibility (64/66, 97%; 53/54, 98%; and 52/63, 83%, respectively; P=.002). All displayed episodes of hallucinations. ChatGPT-3.5 and ChatGPT-4 were then rechallenged with a prompt to adapt their language to the audience and to provide source of information, and responses were rated. ChatGPT-3.5 showed better ability to adapt its language to nonmedical audience than ChatGPT-4 (17/21, 81% and 10/22, 46%, respectively; P=.03); however, both failed to consistently provide correct and up-to-date information resources, reporting either out-of-date materials, incorrect URLs, or unfocused references, making their output not verifiable by the reader. CONCLUSIONS: In conclusion, despite LLMs' potential capability in confronting challenging medical topics such as HSCT, the presence of mistakes and lack of clear references make them not yet appropriate for routine, unsupervised clinical use, or patient counseling. Implementation of LLMs' ability to access and to reference current and updated websites and research papers, as well as development of LLMs trained in specialized domain knowledge data sets, may offer potential solutions for their future clinical application.

Implementation of a health information technology safety classification system in the Veterans Health Administration's Informatics Patient Safety Office.

OBJECTIVE: Implement the 5-type health information technology (HIT) patient safety concern classification system for HIT patient safety issues reported to the Veterans Health Administration's Informatics Patient Safety Office. MATERIALS AND METHODS: A team of informatics safety analysts retrospectively classified 1 year of HIT patient safety issues by type of HIT patient safety concern using consensus discussions. The processes established during retrospective classification were then applied to incoming HIT safety issues moving forward. RESULTS: Of 140 issues retrospectively reviewed, 124 met the classification criteria. The majority were HIT failures (eg, software defects) (33.1%) or configuration and implementation problems (29.8%). Unmet user needs and external system interactions accounted for 20.2% and 10.5%, respectively. Absence of HIT safety features accounted for 2.4% of issues, and 4% did not have enough information to classify. CONCLUSION: The 5-type HIT safety concern classification framework generated actionable categories helping organizations effectively respond to HIT patient safety risks.

Predicting drug-target interactions using matrix factorization with self-paced learning and dual similarity information.

BACKGROUND: Drug repositioning (DR) refers to a method used to find new targets for existing drugs. This method can effectively reduce the development cost of drugs, save time on drug development, and reduce the risks of drug design. The traditional experimental methods related to DR are time-consuming, expensive, and have a high failure rate. Several computational methods have been developed with the increase in data volume and computing power. In the last decade, matrix factorization (MF) methods have been widely used in DR issues. However, these methods still have some challenges. (1) The model easily falls into a bad local optimal solution due to the high noise and high missing rate in the data. (2) Single similarity information makes the learning power of the model insufficient in terms of identifying the potential associations accurately. OBJECTIVE: We proposed self-paced learning with dual similarity information and MF (SPLDMF), which introduced the self-paced learning method and more information related to drugs and targets into the model to improve prediction performance. METHODS: Combining self-paced learning first can effectively alleviate the model prone to fall into a bad local optimal solution because of the high noise and high data missing rate. Then, we incorporated more data into the model to improve the model's capacity for learning. RESULTS: Our model achieved the best results on each dataset tested. For example, the area under the receiver operating characteristic curve and the precision-recall curve of SPLDMF was 0.982 and 0.815, respectively, outperforming the state-of-the-art methods. CONCLUSION: The experimental results on five benchmark datasets and two extended datasets demonstrated the effectiveness of our approach in predicting drug-target interactions.