Network state dynamics underpin basal craving in a transdiagnostic population.

Emerging fMRI methods quantifying brain dynamics present an opportunity to capture how fluctuations in brain responses give rise to individual variations in affective and motivation states. Although the experience and regulation of affective states affect psychopathology, their underlying time-varying brain responses remain unclear. Here, we present a novel framework to identify network states matched to an affective experience and examine how the dynamic engagement of these network states contributes to this experience. We apply this framework to investigate network state dynamics underlying basal craving, an affective experience with important clinical implications. In a transdiagnostic sample of healthy controls and individuals diagnosed with or at risk for craving-related disorders (total N = 252), we utilized connectome-based predictive modeling (CPM) to identify brain networks predictive of basal craving. An edge-centric timeseries approach was leveraged to quantify the moment-to-moment engagement of the craving-positive and craving-negative subnetworks during independent scan runs. We found that dynamic markers of network engagement, namely more persistence in a craving-positive network state and less dwelling in a craving-negative network state, characterized individuals with higher craving. We replicated the latter results in a separate dataset, incorporating distinct participants (N = 173) and experimental stimuli. The associations between basal craving and network state dynamics were consistently observed even when craving-predictive networks were defined in the replication dataset. These robust findings suggest that network state dynamics underpin individual differences in basal craving. Our framework additionally presents a new avenue to explore how the moment-to-moment engagement of behaviorally meaningful network states supports our affective experiences.

Evaluating fluvoxamine for the outpatient treatment of COVID-19: A systematic review and meta-analysis.

This systematic review and meta-analysis of randomised controlled trials (RCTs) aimed to evaluate the efficacy, safety, and tolerability of fluvoxamine for the outpatient management of COVID-19. We conducted this review in accordance with the PRISMA 2020 guidelines. Literature searches were conducted in MEDLINE, EMBASE, International Pharmaceutical Abstracts, CINAHL, Web of Science, and CENTRAL up to 14 September 2023. Outcomes included incidence of hospitalisation, healthcare utilization (emergency room visits and/or hospitalisation), mortality, supplemental oxygen and mechanical ventilation requirements, serious adverse events (SAEs) and non-adherence. Fluvoxamine 100 mg twice a day was associated with reductions in the risk of hospitalisation (risk ratio [RR] 0.75, 95% confidence interval [CI] 0.58-0.97; I 2  = 0%) and reductions in the risk of healthcare utilization (RR 0.68, 95% CI 0.53-0.86; I 2  = 0%). While no increased SAEs were observed, fluvoxamine 100 mg twice a day was associated with higher treatment non-adherence compared to placebo (RR 1.61, 95% CI 1.22-2.14; I 2  = 53%). In subgroup analyses, fluvoxamine reduced healthcare utilization in outpatients with BMI ≥30 kg/m2 , but not in those with lower BMIs. While fluvoxamine offers potential benefits in reducing healthcare utilization, its efficacy may be most pronounced in high-risk patient populations. The observed non-adherence rates highlight the need for better patient education and counselling. Future investigations should reassess trial endpoints to include outcomes relating to post-COVID sequelaes. Registration: This review was prospectively registered on PROSPERO (CRD42023463829).

Automated Paper Screening for Clinical Reviews Using Large Language Models: Data Analysis Study.

BACKGROUND: The systematic review of clinical research papers is a labor-intensive and time-consuming process that often involves the screening of thousands of titles and abstracts. The accuracy and efficiency of this process are critical for the quality of the review and subsequent health care decisions. Traditional methods rely heavily on human reviewers, often requiring a significant investment of time and resources. OBJECTIVE: This study aims to assess the performance of the OpenAI generative pretrained transformer (GPT) and GPT-4 application programming interfaces (APIs) in accurately and efficiently identifying relevant titles and abstracts from real-world clinical review data sets and comparing their performance against ground truth labeling by 2 independent human reviewers. METHODS: We introduce a novel workflow using the Chat GPT and GPT-4 APIs for screening titles and abstracts in clinical reviews. A Python script was created to make calls to the API with the screening criteria in natural language and a corpus of title and abstract data sets filtered by a minimum of 2 human reviewers. We compared the performance of our model against human-reviewed papers across 6 review papers, screening over 24,000 titles and abstracts. RESULTS: Our results show an accuracy of 0.91, a macro F1-score of 0.60, a sensitivity of excluded papers of 0.91, and a sensitivity of included papers of 0.76. The interrater variability between 2 independent human screeners was κ=0.46, and the prevalence and bias-adjusted κ between our proposed methods and the consensus-based human decisions was κ=0.96. On a randomly selected subset of papers, the GPT models demonstrated the ability to provide reasoning for their decisions and corrected their initial decisions upon being asked to explain their reasoning for incorrect classifications. CONCLUSIONS: Large language models have the potential to streamline the clinical review process, save valuable time and effort for researchers, and contribute to the overall quality of clinical reviews. By prioritizing the workflow and acting as an aid rather than a replacement for researchers and reviewers, models such as GPT-4 can enhance efficiency and lead to more accurate and reliable conclusions in medical research.

Assessing the research landscape and clinical utility of large language models: a scoping review.

IMPORTANCE: Large language models (LLMs) like OpenAI's ChatGPT are powerful generative systems that rapidly synthesize natural language responses. Research on LLMs has revealed their potential and pitfalls, especially in clinical settings. However, the evolving landscape of LLM research in medicine has left several gaps regarding their evaluation, application, and evidence base. OBJECTIVE: This scoping review aims to (1) summarize current research evidence on the accuracy and efficacy of LLMs in medical applications, (2) discuss the ethical, legal, logistical, and socioeconomic implications of LLM use in clinical settings, (3) explore barriers and facilitators to LLM implementation in healthcare, (4) propose a standardized evaluation framework for assessing LLMs' clinical utility, and (5) identify evidence gaps and propose future research directions for LLMs in clinical applications. EVIDENCE REVIEW: We screened 4,036 records from MEDLINE, EMBASE, CINAHL, medRxiv, bioRxiv, and arXiv from January 2023 (inception of the search) to June 26, 2023 for English-language papers and analyzed findings from 55 worldwide studies. Quality of evidence was reported based on the Oxford Centre for Evidence-based Medicine recommendations. FINDINGS: Our results demonstrate that LLMs show promise in compiling patient notes, assisting patients in navigating the healthcare system, and to some extent, supporting clinical decision-making when combined with human oversight. However, their utilization is limited by biases in training data that may harm patients, the generation of inaccurate but convincing information, and ethical, legal, socioeconomic, and privacy concerns. We also identified a lack of standardized methods for evaluating LLMs' effectiveness and feasibility. CONCLUSIONS AND RELEVANCE: This review thus highlights potential future directions and questions to address these limitations and to further explore LLMs' potential in enhancing healthcare delivery.

Epidemiology and Healthcare Expenditure for Skin Disease in Emergency Departments in Alberta, Canada.

BACKGROUND: There are limited data on the epidemiology and costs associated with managing dermatologic conditions in emergency departments (EDs). OBJECTIVE: To assess the incidence and mean cost per case of skin diseases in EDs in Alberta. METHODS: Alberta Health Services' Interactive Health Data Application was used to determine the epidemiology and costs associated with nonneoplastic dermatologic diseases in EDs in the province of Alberta, Canada, from 2018 to 2022. Skin conditions were identified using the International Classification of Disease 10th edition diagnostic groupings. RESULTS: Skin disease represented 3.59% of all ED presentations in Alberta in 2022. The total costs associated with managing dermatologic conditions have remained stable over time at approximately 15 million Canadian Dollars (CAD) annually, but the mean cost per case has risen from 188.88 (SD 15.42) in 2018 to 246.25 CAD (SD 27.47) in 2022 (7.59%/year). Infections of skin and subcutaneous tissue were the most expensive diagnostic grouping. The most common dermatologic diagnostic groupings presenting to the ED were infections of skin and subcutaneous tissue [mean age-standardized incidence rate (ASIR) of 143.67 per 100,000 standard population (SD 241.99)], urticaria and erythema [mean ASIR 33.57 per 100,000 standard population (SD 59.13)], and dermatitis and eczema [mean ASIR 18.59 per 100,000 standard population (SD 23.65)]. Cellulitis was both the most common and the costliest individual diagnosis. The majority of patients were triaged as less urgent or nonurgent. CONCLUSIONS: Skin disease represents a substantial public health burden in EDs. Further research into drivers of cost change and areas for cost savings is essential.

Medico-Legal Complaints Against Dermatologists: Data From the Canadian Medical Protective Association, 2013 to 2022.

BACKGROUND: Medico-legal complaints against physicians are a significant source of anxiety and could be associated with defensive medical practices that may correlate with poor patient outcomes. Little is known about patient concerns brought to regulatory bodies and courts against dermatologists in Canada. OBJECTIVE: To characterize factors contributing to medico-legal complaints brought against dermatologists in Canada. METHODS: The Canadian Medical Protective Association (CMPA) repository was queried for all closed cases involving dermatologists over the past decade. Aggregate, anonymized data was reviewed and case outcomes, patient harm, and contributing factors were extracted. RESULTS: Nearly one-fifth of all dermatologists who are CMPA members have been named in at least one medico-legal case between 2013 to 2022. A total of 396 civil-legal actions or College complaint cases involving dermatologists were closed at the CMPA during this timeframe. The most common patient allegations were deficient assessment (34%), diagnostic error (28%), and unprofessional manner (22%). Nearly half of patients experienced a harmful event, the majority of which were asymptomatic or mild. The most frequently identified contributing factors related to providers were poor clinical decision making (n = 73), lack of situational awareness (n = 67), and conduct and boundary issues (n = 59). Team factors included a breakdown of communication with patients (n = 124). CONCLUSIONS: Improved communication with patients for informed consent, treatment plans, clinical follow-up, and documentation of thorough clinical patient assessments can improve patient satisfaction and health outcomes, and mitigate dermatologists' medico-legal risk.

Temporal and regional trends in Canada for the epidemiology and management of diabetic foot ulcers.

OBJECTIVE: Diabetic foot ulcers (DFUs) are a common downstream consequence of diabetes and pose significant concern to the health of affected individuals. There are currently limited data available that detail the epidemiology and inpatient burden of DFUs in Canada. This study aims to provide updated data on the epidemiological and economic burden of DFUs in Canada between 2015 and 2019. METHOD: Using the Canadian Institute for Health Information Patient Cost Estimator the authors estimated, as the primary outcome, the number of DFU inpatient cases in Canada, the associated financial burden of these admissions on the Canadian healthcare system, physician compensation, and average patient length of hospitalisation. This analysis covered 12 Canadian jurisdictions and was stratified by age, sex, province and geographical region. The secondary outcome was to highlight temporal trends in the public health burden of DFUs by computing the average annual percentage change (AAPC; the weighted average of several annual percentage changes over multiple years) using Joinpoint (Surveillance Research Program National Cancer Institute, US) regression analysis. RESULTS: The total number of cases, average length of hospitalisation and physician costs across Canada were highest for patients over ≥60 years of age. By region, the total number of cases and standardised physician costs were highest in Central Canada, followed by Western Canada, and subsequently Eastern and Northern Canada. In 2019, there were >1800 patients with DFUs admitted to Canadian acute care hospitals. Despite having the lowest number of inpatient admissions, Northern Canada had the highest associated inpatient costs, followed by Central, then Western and, lastly, Eastern Canada. Overall, mean inpatient costs remained stable over time across all age groups (AAPC 0.61; 95% confidence interval: -1.87-3.15), with an average cost of >$10,000 CAD per case. Average physician cost across all jurisdictions was approximately $1000 CAD per case, with the mean hospitalisation time being nine days. CONCLUSION: The findings of this study emphasise the dynamic nature of the economic and epidemiological DFU burden in Canada, underscoring the need for targeted interventions, multidisciplinary care and evidence-based resource allocation for the optimal management of diabetes and DFUs.

Effects of Dopaminergic Drugs on Cognitive Control Processes Vary by Genotype.

Dopamine (DA) has been implicated in modulating multiple cognitive control processes, including the robust maintenance of task sets and memoranda in the face of distractors (cognitive stability) and, conversely, the ability to switch task sets or update the contents of working memory when it is advantageous to do so (cognitive flexibility). In humans, the limited specificity of available pharmacological probes has posed a challenge for understanding the mechanisms by which DA, acting on multiple receptor families across the PFC and striatum, differentially influences these cognitive processes. Using a within-subject, placebo-controlled design, we contrasted the impact of two mechanistically distinct DA drugs, tolcapone (an inhibitor of catechol-O-methyltransferase [COMT], a catecholamine inactivator) and bromocriptine (a DA agonist with preferential affinity for the D2 receptor), on the maintenance and switching of task rules. Given previous work demonstrating that drug effects on behavior are dependent on baseline DA tone, participants were stratified according to genetic polymorphisms associated with cortical (COMT Val158Met) and striatal (Taq1A) DA system function. Our results were partially consistent with an inverted-U-shaped relationship between tolcapone and robust rule maintenance (interaction with COMT genotype) and between bromocriptine and cued rule switching (interaction with Taq1A genotype). However, when task instructions were ambiguous, a third relationship emerged to explain drug effects on spontaneous task switching (interaction of COMT genotype and bromocriptine). Together, this pattern of results suggests that the effects of DA drugs vary not only as a function of the DA system component upon which they act but also on subtle differences in task demands and context.

Design, evaluation, and application of engineered skeletal muscle.

For over two decades, research groups have been developing methods to engineer three-dimensional skeletal muscle tissues. These tissues hold great promise for use in disease modeling and pre-clinical drug development, and have potential to serve as therapeutic grafts for functional muscle repair. Recent advances in the field have resulted in the engineering of regenerative muscle constructs capable of survival, vascularization, and functional maturation in vivo as well as the first-time creation of functional human engineered muscles for screening of therapeutics in vitro. In this review, we will discuss the methodologies that have progressed work in the muscle tissue engineering field to its current state. The emphasis will be placed on the existing procedures to generate myogenic cell sources and form highly functional muscle tissues in vitro, techniques to monitor and evaluate muscle maturation and performance in vitro and in vivo, and surgical strategies to both create diseased environments and ensure implant survival and rapid integration into the host. Finally, we will suggest the most promising methodologies that will enable continued progress in the field.

Development of a Simulation Scenario and Evaluation Checklist for Patients With Asthma in Emergency Care.

Care for patients with asthma requires prompt and effective decision-making abilities. Many nursing students report a lack of preparedness and competence with respect to their ability to perform nursing duties. Simulation-based learning has been developed as a potential solution to this problem. Moreover, the simulation scenario with an evaluation checklist offers a reliable evaluation strategy for the education and training of clinical decision-making skills among nursing students. The simulation scenario and evaluation checklist developed in this study offer a means to improve patient safety during asthma care in the emergency care unit, as well as to improve nursing students' problem-solving competencies and communication with patients with asthma.

Contrasting neurofunctional correlates of face- and visuospatial-processing in children and adolescents with Williams syndrome: convergent results from four fMRI paradigms.

Understanding neurogenetic mechanisms underlying neuropsychiatric disorders such as schizophrenia and autism is complicated by their inherent clinical and genetic heterogeneity. Williams syndrome (WS), a rare neurodevelopmental condition in which both the genetic alteration (hemideletion of ~ twenty-six 7q11.23 genes) and the cognitive/behavioral profile are well-defined, offers an invaluable opportunity to delineate gene-brain-behavior relationships. People with WS are characterized by increased social drive, including particular interest in faces, together with hallmark difficulty in visuospatial processing. Prior work, primarily in adults with WS, has searched for neural correlates of these characteristics, with reports of altered fusiform gyrus function while viewing socioemotional stimuli such as faces, along with hypoactivation of the intraparietal sulcus during visuospatial processing. Here, we investigated neural function in children and adolescents with WS by using four separate fMRI paradigms, two that probe each of these two cognitive/behavioral domains. During the two visuospatial tasks, but not during the two face processing tasks, we found bilateral intraparietal sulcus hypoactivation in WS. In contrast, during both face processing tasks, but not during the visuospatial tasks, we found fusiform hyperactivation. These data not only demonstrate that previous findings in adults with WS are also present in childhood and adolescence, but also provide a clear example that genetic mechanisms can bias neural circuit function, thereby affecting behavioral traits.

The brain structure, inflammatory, and genetic mechanisms mediate the association between physical frailty and depression.

Cross-sectional studies have demonstrated strong associations between physical frailty and depression. However, the evidence from prospective studies is limited. Here, we analyze data of 352,277 participants from UK Biobank with 12.25-year follow-up. Compared with non-frail individuals, pre-frail and frail individuals have increased risk for incident depression independent of many putative confounds. Altogether, pre-frail and frail individuals account for 20.58% and 13.16% of depression cases by population attributable fraction analyses. Higher risks are observed in males and individuals younger than 65 years than their counterparts. Mendelian randomization analyses support a potential causal effect of frailty on depression. Associations are also observed between inflammatory markers, brain volumes, and incident depression. Moreover, these regional brain volumes and three inflammatory markers-C-reactive protein, neutrophils, and leukocytes-significantly mediate associations between frailty and depression. Given the scarcity of curative treatment for depression and the high disease burden, identifying potential modifiable risk factors of depression, such as frailty, is needed.

The use of large language models in medicine: proceeding with caution.

Large language models, like ChatGPT and Bard, have potential clinical applications due to their ability to generate conversational responses and encode medical knowledge. However, their clinical adoption faces challenges including hallucinations, lack of transparency, and lack of consistency. Ethicolegal concerns surrounding patient consent, legal liability, and data privacy further complicate matters. Despite their promise, an optimistic but cautious approach is essential for the safe integration of large language models into clinical settings.

Corrigendum: Nocebo effects in systemic therapies for adult plaque psoriasis: a systematic review and meta-analysis.

[This corrects the article DOI: 10.3389/fmed.2024.1373520.].

Nocebo effects in systemic therapies for adult plaque psoriasis: A systematic review and meta-analysis.

Introduction: The nocebo effect is defined as adverse outcomes secondary to negative patient expectations rather than the pharmacologic activity of an intervention. Nocebo effects can reduce treatment adherence and/or persistence. Therefore, nocebo effects in psoriasis need to be defined. Methods: A Cochrane systematic review was updated with a search of MEDLINE, Embase, and the CENTRAL Register of Controlled Trials for phase II - IV RCTs comparing systemic therapy versus placebo for patients with moderate-to-severe plaque psoriasis. Estimates were pooled using a random effects model, and heterogeneity was evaluated using the I2 statistic. The primary outcome was the pooled proportion of any adverse event (AE) and corresponding risk difference (RD) in patients randomized to placebo versus systemic therapy. Results: A total of 103 unique trials were identified enrolling 43,189 patients. The overall pooled AE rate in patients randomized to systemic therapy was 57.1% [95% CI: 54.7-59.5%] compared to 49.8% [95% CI: 47.1-52.4%] for placebo [RD 6.7% (95% CI: 4.6-8.9%), p < 0.00001, I2 = 75%]. Both biologic and non-biologic systemic therapy groups had a higher proportion of infectious AEs compared to placebo. No statistically significant RD in serious AEs or AEs leading to discontinuation was identified between systemic therapy and placebo groups. Discussion: Half of patients exposed to inert placebo in clinical trials of systemic psoriasis therapies experienced AEs, which may be explained by nocebo effects. These findings have important implications when counseling patients and designing future studies.

Investigating brain dynamics and their association with cognitive control in opioid use disorder using naturalistic and drug cue paradigms.

Objectives: Opioid use disorder (OUD) impacts millions of people worldwide. The prevalence and debilitating effects of OUD present a pressing need to understand its neural mechanisms to provide more targeted interventions. Prior studies have linked altered functioning in large-scale brain networks with clinical symptoms and outcomes in OUD. However, these investigations often do not consider how brain responses change over time. Time-varying brain network engagement can convey clinically relevant information not captured by static brain measures. Methods: We investigated brain dynamic alterations in individuals with OUD by applying a new multivariate computational framework to movie-watching (i.e., naturalistic; N=76) and task-based (N=70) fMRI. We further probed the associations between cognitive control and brain dynamics during a separate drug cue paradigm in individuals with OUD. Results: Compared to healthy controls (N=97), individuals with OUD showed decreased variability in the engagement of recurring brain states during movie-watching. We also found that worse cognitive control was linked to decreased variability during the rest period when no opioid-related stimuli were present. Conclusions: These findings suggest that individuals with OUD may experience greater difficulty in effectively engaging brain networks in response to evolving internal or external demands. Such inflexibility may contribute to aberrant response inhibition and biased attention toward opioid-related stimuli, two hallmark characteristics of OUD. By incorporating temporal information, the current study introduces novel information about how brain dynamics are altered in individuals with OUD and their behavioral implications.

Variation in moment-to-moment brain state engagement changes across development and contributes to individual differences in executive function.

Neural variability, or variation in brain signals, facilitates dynamic brain responses to ongoing demands. This flexibility is important during development from childhood to young adulthood, a period characterized by rapid changes in experience. However, little is known about how variability in the engagement of recurring brain states changes during development. Such investigations would require the continuous assessment of multiple brain states concurrently. Here, we leverage a new computational framework to study state engagement variability (SEV) during development. A consistent pattern of SEV changing with age was identified across cross-sectional and longitudinal datasets (N>3000). SEV developmental trajectories stabilize around mid-adolescence, with timing varying by sex and brain state. SEV successfully predicts executive function (EF) in youths from an independent dataset. Worse EF is further linked to alterations in SEV development. These converging findings suggest SEV changes over development, allowing individuals to flexibly recruit various brain states to meet evolving needs.