Implementation and evaluation of an interprofessional prescription writing workshop with a simulated electronic prescribing activity for preclerkship medical students.

BACKGROUND: Prescription writing skills are essential for physician practice. This study describes the development and implementation of a curricular intervention focused on improving the knowledge and confidence of preclerkship medical students' prescription writing practices utilizing an interprofessional education model, with a focus on electronic prescribing. METHODS: Medicine and Pharmacy Faculty from a large, urban university collaborated to develop the content of the workshop and a simulation platform was used for the e-prescribing activity. Second-year medical students attended a mandatory in-person workshop facilitated by fourth-year pharmacy students. A pre and post knowledge test and confidence survey were used to assess students' knowledge, confidence, and satisfaction. Outcomes from the knowledge test were evaluated with paired-samples proportions tests, and confidence survey data was evaluated with paired t-tests and Wilcoxon signed-rank tests in a pre-post study design. RESULTS: Students demonstrated a significant increase in prescription writing knowledge and confidence after completing the workshop. On the pre-test, 7% of students (21/284) completed the electronic prescribing assessment correctly and 51% of students (149/295) completed it correctly on the post-test. All items on the confidence survey showed a significant increase in pre- versus post-survey comparisons (p < 0.001). CONCLUSIONS: This interprofessional prescription writing workshop facilitated by pharmacy students shows promise for improving the knowledge and confidence of prescription writing and electronic prescribing practices in preclerkship medical students.

Assessment of confidence in medical writing: Development and validation of the first trustworthy measurement tool.

BACKGROUND: The popularity of medical writing workshops highlights the need for a standard measurement tool to assess the impact of such workshops on participants' confidence in: 1- writing a standard article and 2- using optimal English language. Because such an instrument is not yet available, we undertook this study to devise and evaluate the first measurement tool to assess such confidence. METHOD: We created an item pool of 50 items by searching Medline, Embase, and Clarivate Analytics to find related articles, using our prior experience, and approaching the key informants. We revised and edited the item pool, and redundant ones were excluded. Finally, the 36-item tool comprised two domains. We tested it in a group of workshop applicants for internal consistency and temporal reliability using Cronbach's α and Pearson correlations and for content and convergent validity using the content validity index and Pearson correlations. RESULTS: The participants had a mean age of 40.3 years, a female predominance (74.3%), and a majority of faculty members (51.4%). The internal consistency showed high reliability (> 0.95). Test-retest reliability showed very high correlations (r = 0.93). The CVI for domain 1 was 0.78, for domain 2 was 0.73, and for the entire instrument was 0.75. CONCLUSION: This unique, reliable, and valid measurement tool could accurately measure the level of confidence in writing a standard medical article and in using the appropriate English language for this purpose.

Importance of Patient History in Artificial Intelligence-Assisted Medical Diagnosis: Comparison Study.

Background: Medical history contributes approximately 80% to a diagnosis, although physical examinations and laboratory investigations increase a physician's confidence in the medical diagnosis. The concept of artificial intelligence (AI) was first proposed more than 70 years ago. Recently, its role in various fields of medicine has grown remarkably. However, no studies have evaluated the importance of patient history in AI-assisted medical diagnosis. Objective: This study explored the contribution of patient history to AI-assisted medical diagnoses and assessed the accuracy of ChatGPT in reaching a clinical diagnosis based on the medical history provided. Methods: Using clinical vignettes of 30 cases identified in The BMJ, we evaluated the accuracy of diagnoses generated by ChatGPT. We compared the diagnoses made by ChatGPT based solely on medical history with the correct diagnoses. We also compared the diagnoses made by ChatGPT after incorporating additional physical examination findings and laboratory data alongside history with the correct diagnoses. Results: ChatGPT accurately diagnosed 76.6% (23/30) of the cases with only the medical history, consistent with previous research targeting physicians. We also found that this rate was 93.3% (28/30) when additional information was included. Conclusions: Although adding additional information improves diagnostic accuracy, patient history remains a significant factor in AI-assisted medical diagnosis. Thus, when using AI in medical diagnosis, it is crucial to include pertinent and correct patient histories for an accurate diagnosis. Our findings emphasize the continued significance of patient history in clinical diagnoses in this age and highlight the need for its integration into AI-assisted medical diagnosis systems.

Metacognitive evaluation of postdecisional perceptual representations.

Perceptual confidence is thought to arise from metacognitive processes that evaluate the underlying perceptual decision evidence. We investigated whether metacognitive access to perceptual evidence is constrained by the hierarchical organization of visual cortex, where high-level representations tend to be more readily available for explicit scrutiny. We found that the ability of human observers to evaluate their confidence did depend on whether they performed a high-level or low-level task on the same stimuli, but was also affected by manipulations that occurred long after the perceptual decision. Confidence in low-level perceptual decisions degraded with more time between the decision and the response cue, especially when backward masking was present. Confidence in high-level tasks was immune to backward masking and benefitted from additional time. These results can be explained by a model assuming confidence heavily relies on postdecisional internal representations of visual stimuli that degrade over time, where high-level representations are more persistent.

Ontological Psychoanalysis in Clinical Practice.

The author describes and then clinically illustrates what he terms the ontological dimension of psychoanalysis (having to do with coming into being) and the epistemological dimension of psychoanalysis (having to do with coming to know and understand). Neither of these dimensions of psychoanalysis exists in pure form; they are inextricably intertwined. Epistemological psychoanalysis, for which Freud and Klein are the principal architects, involves the work of arriving at understandings of play, dreams, and associations; while ontological psychoanalysis, for which Winnicott and Bion are the principal architects, involves creating conditions in which the patient might become more fully alive and real to him- or herself. The author provides clinical illustrations of the ontological dimension of psychoanalysis in which the process of the patient's coming more fully into being is facilitated by the experiences in which the patient feels recognized for the individual he is and is becoming. This occurs in an analysis in which the analyst and patient invent a form of psychoanalysis that is uniquely their own.

What We Do, What We Say, What We Don't Say: Confidentiality In The Publication Of Clinical Writing.

Questions concerning analysts' publication of material from the analyses of their patients have troubled the field of psychoanalysis since its inception. Disguise inevitably distorts the clinical material and is often insufficient to protect the patient from recognition. Asking the patient's consent for publication intrudes upon and alters the analytic process. While analysts have largely reached a consensus about the need for anonymity in published material, there is still considerable debate about the necessity for obtaining patients' consent when using their material for publication. In this paper, I will trace the evolving meanings of disguise, and particularly of consent, in the analytic literature. I will place a particular emphasis upon the differing theoretical belief systems that underlie the analyst's decision to ask consent from her patient or not to do so, and I will argue that, although decisions on asking consent remain a complex matter, such coherent belief systems should play an important part in analysts' decisions regarding consent. I will illustrate my thought processes and some clinical situations with brief examples, and I will conclude with some practical recommendations, with the hope that these will stimulate further discussion in the analytic community.

Resting-state effective connectivity is systematically linked to reappraisal success of high- and low-intensity negative emotions.

Emotion regulation is a process by which individuals modulate their emotional responses to cope with different environmental demands, for example, by reappraising the emotional situation. Here, we tested whether effective connectivity of a reappraisal-related neural network at rest is predictive of successfully regulating high- and low-intensity negative emotions in an emotion-regulation task. Task-based and resting-state functional magnetic resonance imaging (rs-fMRI) data of 28 participants were collected using ultra-high magnetic field strength at 7 Tesla during three scanning sessions. We used spectral dynamic causal modeling (spDCM) on the rs-fMRI data within brain regions modulated by emotion intensity. We found common connectivity patterns for both high- and low-intensity stimuli. Distinctive effective connectivity patterns in relation to low-intensity stimuli were found from frontal regions connecting to temporal regions. Reappraisal success for high-intensity stimuli was predicted by additional connections within the vlPFC and from temporal to frontal regions. Connectivity patterns at rest predicting reappraisal success were generally more pronounced for low-intensity stimuli, suggesting a greater role of stereotyped patterns, potentially reflecting preparedness, when reappraisal was relatively easy to implement. The opposite was true for high-intensity stimuli, which might require a more flexible recruitment of resources beyond what is reflected in resting state connectivity patterns. Resting-state effective connectivity emerged as a robust predictor for successful reappraisal, revealing both shared and distinct network dynamics for high- and low-intensity stimuli. These patterns signify specific preparatory states associated with heightened vigilance, attention, self-awareness, and goal-directed cognitive processing, particularly during reappraisal for mitigating the emotional impact of external stimuli. Our findings hold potential implications for understanding psychopathological alterations in brain connectivity related to affective disorders.

Recruitment of the premotor cortex during arithmetic operations by the monkey.

Arithmetic operations are complex mental processes rooted in the abstract concept of numerosity. Despite the significance, the neural architecture responsible for these operations has remained largely uncharted. In this study, we explored the presence of specific neuronal activity in the dorsal premotor cortex of the monkey dedicated to numerical addition and subtraction. Our findings reveal that many of these neural activities undergo a transformation, shifting their coding from arithmetic to motor representations. These motor representations include information about which hand to use and the number of steps involved in the action. We consistently observed that cells related to the right-hand encoded addition, while those linked to the left-hand encoded subtraction, suggesting that arithmetic operations and motor commands are intertwining with each other. Furthermore, we used a multivariate decoding technique to predict the monkey's behaviour based on the activity of these arithmetic-related cells. The classifier trained to discern arithmetic operations, including addition and subtraction, not only predicted the arithmetic decisions but also the subsequent motor actions of the right and left-hand. These findings imply a cognitive extension of the motor cortex's function, where inherent neural systems are repurposed to facilitate arithmetic operations.

Trends in U.S. public confidence in science and opportunities for progress.

In recent years, many questions have been raised about whether public confidence in science is changing. To clarify recent trends in the public's confidence and factors that are associated with these feelings, an effort initiated by the National Academies' Strategic Council for Research Excellence, Integrity, and Trust (the Strategic Council) analyzed findings from multiple survey research organizations. The Strategic Council's effort, which began in 2022, found that U.S. public confidence in science, the scientific community, and leaders of scientific communities is high relative to other civic, cultural, and governmental institutions for which researchers regularly collect such data. However, confidence in these institutions has fallen during the previous 5 years. Science's decline, while real, is similar to or less than that in the other groups. A recent study goes into greater detail by exploring public views of science. From these data, we observe that many of the surveyed U.S. public question the extent to which scientists share their values or overcome personal biases when presenting conclusions. At the same time, large majorities agree on certain types of actions that they want scientists to take. For example, 84% respond that it is "somewhat important" or "very important" for scientists to disclose their funders. Ninety-two percent (92%) offer the same responses to scientists "being open to changing their minds based on new evidence." Collectively, these data clarify how the U.S. public views science and scientists. They also suggest actions that can affect public confidence in science and scientists in the years to come.

Asthma Academy for Family Caregivers: A Quality Improvement Project.

Background: Asthma affects one in every 12 children in United States with significant prevalence in underserved groups. Family caregiver education is essential to improve childhood asthma control. The literature suggests that family caregivers' lack of knowledge about asthma management affects their children's health outcomes. An evidence-based education program that improves caregivers' asthma knowledge may improve the family's well-being. Objectives: The goal of the Asthma Academy project is to improve the disease management of children with asthma by providing an in-person family caregiver education program. The main objectives are to (a) improve family caregivers' asthma knowledge, (b) enhance their confidence in asthma management, and (c) evaluate caregivers' satisfaction with the education program. Method: This quality improvement project used an in-person educational program with an asthma education video resource for caregivers of children with asthma. Results: The family caregivers' asthma knowledge improved significantly after the education. The confidence levels in caring for children with asthma increased. Caregivers' satisfaction with the Asthma Academy education session was favorable. Conclusions: The Asthma Academy was an acceptable and effective delivery method of education for family caregivers of children with asthma in the in-patient setting. Empowering family caregivers of vulnerable children through asthma education may improve child health outcomes and mitigate complications from asthma. Implications for Nursing Nurses and clinicians are steward of providing patient education. Individualized, patient-tailored education is recommended.

A study on the purchase intention of luxury goods from the perspective of face perception and expected regret.

The study on the impact of consumer purchase intention on luxury goods has received widespread attention from the academic community. This study collected research data in Guilin, China, through questionnaire survey, and conducted an empirical study on the influencing factors of luxury consumers' purchase intention. The results show: The price level of luxury goods has a positive impact on consumers' face perception, while the positive impact of price level on expected regret has not been verified. Consumer's face perception has positive and negative effects on consumers' expected regret and consumers' purchase intention respectively. Consumer's downward expected regret and consumer's upward expected regret have different effects on consumers' purchase intention. Consumers' face perception and expected regret play a mediating effect in the research of influence relationship. This study is conducive to a better analysis of the psychology and behavior of Chinese luxury consumers, enriching the theoretical connotation of consumer psychology, and promoting the healthy development of the luxury goods industry.

A quantitative comparison of virtual and physical experimental paradigms for the investigation of pedestrian responses in hostile emergencies.

Modern experiments investigating human behaviour in emergencies are often implemented in virtual reality (VR), due to the increased experimental control and improved ethical viability over physical reality (PR). However, there remain questions regarding the validity of the results obtained from these environments, and no full validation of VR experiments has yet appeared. This study compares the results of two sets of experiments (in VR and PR paradigms) investigating behavioural responses to knife-based hostile aggressors. This study quantitatively analyses these results to ascertain whether the different paradigms generate different responses, thereby assessing the use of virtual reality as a data generating paradigm for emergencies. The results show that participants reported almost identical psychological responses. This study goes on to identify minimal differences in movement responses across a range of predictors, noting a difference in responses between genders. As a result, this study concludes that VR can produce similarly valid data as physical experiments when investigating human behaviour in hostile emergencies, and that it is therefore possible to conduct realistic experimentation through VR environments while retaining confidence in the resulting data. This has major implications for the future of this type of research, and furthermore suggests that VR experimentation should be performed for both existing and new critical infrastructure to understand human responses in hostile scenarios.

An evaluation of the replicability of analyses using synthetic health data.

Synthetic data generation is being increasingly used as a privacy preserving approach for sharing health data. In addition to protecting privacy, it is important to ensure that generated data has high utility. A common way to assess utility is the ability of synthetic data to replicate results from the real data. Replicability has been defined using two criteria: (a) replicate the results of the analyses on real data, and (b) ensure valid population inferences from the synthetic data. A simulation study using three heterogeneous real-world datasets evaluated the replicability of logistic regression workloads. Eight replicability metrics were evaluated: decision agreement, estimate agreement, standardized difference, confidence interval overlap, bias, confidence interval coverage, statistical power, and precision (empirical SE). The analysis of synthetic data used a multiple imputation approach whereby up to 20 datasets were generated and the fitted logistic regression models were combined using combining rules for fully synthetic datasets. The effects of synthetic data amplification were evaluated, and two types of generative models were used: sequential synthesis using boosted decision trees and a generative adversarial network (GAN). Privacy risk was evaluated using a membership disclosure metric. For sequential synthesis, adjusted model parameters after combining at least ten synthetic datasets gave high decision and estimate agreement, low standardized difference, as well as high confidence interval overlap, low bias, the confidence interval had nominal coverage, and power close to the nominal level. Amplification had only a marginal benefit. Confidence interval coverage from a single synthetic dataset without applying combining rules were erroneous, and statistical power, as expected, was artificially inflated when amplification was used. Sequential synthesis performed considerably better than the GAN across multiple datasets. Membership disclosure risk was low for all datasets and models. For replicable results, the statistical analysis of fully synthetic data should be based on at least ten generated datasets of the same size as the original whose analyses results are combined. Analysis results from synthetic data without applying combining rules can be misleading. Replicability results are dependent on the type of generative model used, with our study suggesting that sequential synthesis has good replicability characteristics for common health research workloads.

Maximization delays decision-making in acute care nursing.

The maximization personality trait refers to the tendency to face decision-making situations along a continuum from exhaustively analysing all the options (maximize) to choosing the one that exceeds a subjective threshold of acceptability (satisfy). Research has revealed the influence of maximizing on decision making, although little is known about its possible role in high risk and high uncertainty situations. A sample of 153 active Spanish nurses, with an average experience of 11 years, completed a maximization questionnaire and responded to written vignettes depicting time-demanding decision making in which three options were offered, representing delayed action, non-action, and immediate action. Two vignettes presented critical situations related to acute care during the COVID-19 pandemic, whilst two vignettes presented non-nursing scenarios. People high in maximization took longer to choose and were more likely to choose non-action. No relationship was found between maximization score and the subjective experience of the person making the choice. Maximization had no significant correlation with years of experience nor perceived expertise. Greater perceived expertise was associated with lower indecision and greater confidence. When participants answered nursing vignettes, they took longer to respond, but chose less delayed action and more immediate action. Our results suggest that maximization plays only a relative role in acute care decision-making in nursing, as compared to contextual variables and expertise. They also support a domain general approach to this personality trait. Findings are consistent with Nibbelink and Reed's Practice-Primed Decision Model for nursing.

Updating functional brain units: Insights far beyond Luria.

This paper reviews Luria's model of the three functional units of the brain. To meet this objective, several issues were reviewed: the theory of functional systems and the contributions of phylogenesis and embryogenesis to the brain's functional organization. This review revealed several facts. In the first place, the relationship/integration of basic homeostatic needs with complex forms of behavior. Secondly, the multi-scale hierarchical and distributed organization of the brain and interactions between cells and systems. Thirdly, the phylogenetic role of exaptation, especially in basal ganglia and cerebellum expansion. Finally, the tripartite embryogenetic organization of the brain: rhinic, limbic/paralimbic, and supralimbic zones. Obviously, these principles of brain organization are in contradiction with attempts to establish separate functional brain units. The proposed new model is made up of two large integrated complexes: a primordial-limbic complex (Luria's Unit I) and a telencephalic-cortical complex (Luria's Units II and III). As a result, five functional units were delineated: Unit I. Primordial or preferential (brainstem), for life-support, behavioral modulation, and waking regulation; Unit II. Limbic and paralimbic systems, for emotions and hedonic evaluation (danger and relevance detection and contribution to reward/motivational processing) and the creation of cognitive maps (contextual memory, navigation, and generativity [imagination]); Unit III. Telencephalic-cortical, for sensorimotor and cognitive processing (gnosis, praxis, language, calculation, etc.), semantic and episodic (contextual) memory processing, and multimodal conscious agency; Unit IV. Basal ganglia systems, for behavior selection and reinforcement (reward-oriented behavior); Unit V. Cerebellar systems, for the prediction/anticipation (orthometric supervision) of the outcome of an action. The proposed brain units are nothing more than abstractions within the brain's simultaneous and distributed physiological processes. As function transcends anatomy, the model necessarily involves transition and overlap between structures. Beyond the classic approaches, this review includes information on recent systemic perspectives on functional brain organization. The limitations of this review are discussed.

Analysis of the Psychological Factors Faced by the Final Year College Students of China During Job Interviews and While Choosing Careers.

Career choice research has attracted the attention of recruiters and young graduates. The study aims to investigate the psychological factors that influence college students' employment choices. As a result, data for the study were gathered from 250 final-year college students in China via an online questionnaire survey. The study identified the psychological barriers faced by college students during job interviews through interviews with 120 h recruiters. The SPPS tool is used for data analysis. The study identified personal interest, self-efficacy, and self- esteem; social responsibilities; confidence; professional development opportunities; and future orientation as the important psychological factors that influence the career choice of college students. The study also found that the barriers faced by the college students during the interview were anxiety, inferiority complex, cowardice, and pride. Therefore, the study suggests that the college provides job-oriented training for college students' employment choices. The college should take the initiative to provide students with career opportunities and proper training to avoid psychological barriers during interviews.

Construction and optimization of multi-platform precision pathways for precision medicine.

In the enduring challenge against disease, advancements in medical technology have empowered clinicians with novel diagnostic platforms. Whilst in some cases, a single test may provide a confident diagnosis, often additional tests are required. However, to strike a balance between diagnostic accuracy and cost-effectiveness, one must rigorously construct the clinical pathways. Here, we developed a framework to build multi-platform precision pathways in an automated, unbiased way, recommending the key steps a clinician would take to reach a diagnosis. We achieve this by developing a confidence score, used to simulate a clinical scenario, where at each stage, either a confident diagnosis is made, or another test is performed. Our framework provides a range of tools to interpret, visualize and compare the pathways, improving communication and enabling their evaluation on accuracy and cost, specific to different contexts. This framework will guide the development of novel diagnostic pathways for different diseases, accelerating the implementation of precision medicine into clinical practice.

Robust Federated Learning for Heterogeneous Model and Data.

Data privacy and security is an essential challenge in medical clinical settings, where individual hospital has its own sensitive patients data. Due to recent advances in decentralized machine learning in Federated Learning (FL), each hospital has its own private data and learning models to collaborate with other trusted participating hospitals. Heterogeneous data and models among different hospitals raise major challenges in robust FL, such as gradient leakage, where participants can exploit model weights to infer data. Here, we proposed a robust FL method to efficiently tackle data and model heterogeneity, where we train our model using knowledge distillation and a novel weighted client confidence score on hematological cytomorphology data in clinical settings. In the knowledge distillation, each participant learns from other participants by a weighted confidence score so that knowledge from clean models is distributed other than the noisy clients possessing noisy data. Moreover, we use symmetric loss to reduce the negative impact of data heterogeneity and label diversity by reducing overfitting the model to noisy labels. In comparison to the current approaches, our proposed method performs the best, and this is the first demonstration of addressing both data and model heterogeneity in end-to-end FL that lays the foundation for robust FL in laboratories and clinical applications.

Transcriptional cartography integrates multiscale biology of the human cortex.

The cerebral cortex underlies many of our unique strengths and vulnerabilities, but efforts to understand human cortical organization are challenged by reliance on incompatible measurement methods at different spatial scales. Macroscale features such as cortical folding and functional activation are accessed through spatially dense neuroimaging maps, whereas microscale cellular and molecular features are typically measured with sparse postmortem sampling. Here, we integrate these distinct windows on brain organization by building upon existing postmortem data to impute, validate, and analyze a library of spatially dense neuroimaging-like maps of human cortical gene expression. These maps allow spatially unbiased discovery of cortical zones with extreme transcriptional profiles or unusually rapid transcriptional change which index distinct microstructure and predict neuroimaging measures of cortical folding and functional activation. Modules of spatially coexpressed genes define a family of canonical expression maps that integrate diverse spatial scales and temporal epochs of human brain organization - ranging from protein-protein interactions to large-scale systems for cognitive processing. These module maps also parse neuropsychiatric risk genes into subsets which tag distinct cyto-laminar features and differentially predict the location of altered cortical anatomy and gene expression in patients. Taken together, the methods, resources, and findings described here advance our understanding of human cortical organization and offer flexible bridges to connect scientific fields operating at different spatial scales of human brain research.