Study Protocol for the Residents' Mental Health Investigation, a Dynamic Longitudinal Study in Italy (ReMInDIt).

Medical residents constitute a vulnerable population susceptible to mental health disorders. In Italy, this was evident during the COVID-19 pandemic, when medical residents served on the front line and provided significant support to healthcare services. Therefore, the working group on "Public Mental Health" of the Medical Residents' Council of the Italian Society of Hygiene, Preventive Medicine, and Public Health (S.It.I.) designed the "Residents' mental health investigation, a dynamic longitudinal study in Italy" (ReMInDIt). This longitudinal study aims to assess the mental status of medical residents and to explore potential cause-effect relationships between risk/protective factors (identified among sociodemographic, residency program, and lifestyle characteristics) and mental health outcomes (anxiety and depressive symptoms). Data will be collected from a study population of 3615 residents enrolled in Italian residency programs in public health, occupational medicine, and forensic medicine through an online questionnaire that includes validated tools, requires 10 min for completion, and is disseminated by the residents' Councils. It will be followed by a follow-up administration after 12 months. The ReMInDIt study will play a significant role in generating evidence crucial for enhancing mental health services and promoting protective factors for the mental well-being of this important segment of healthcare professionals.

Digestive Biomarkers of Laryngopharyngeal Reflux: A Preliminary Prospective Controlled Study.

OBJECTIVE: To investigate the digestive enzymes and biomarkers in the saliva of patients with laryngopharyngeal reflux (LPR) and asymptomatic individuals. STUDY DESIGN: Prospective controlled study. SETTING: Multicenter study. METHODS: Patients with LPR at the hypopharyngeal-esophageal impedance-pH monitoring (HEMII-pH) and asymptomatic individuals were consecutively recruited from January 2020 to April 2023 from 2 University Hospitals. The saliva of patients (off PPIs) and asymptomatic individuals was collected to measure pH, elastase, bile salts, cholesterol, gastric, and pancreatic lipases. Anxiety, symptoms, and findings were studied through perceived stress scale (PSS), reflux symptom score (RSS), and reflux sign assessment (RSA). RESULTS: Sixty-seven LPR patients and 57 asymptomatic individuals completed the evaluations. LPR patients reported higher PSS, RSS, and RSA than asymptomatic individuals. The mean saliva pH was more alkaline in LPR patients (7.23: 95% confidence interval [CI]: 7.08, 7.38) compared to controls (6.13; 95% CI: 5.95, 6.31; P = .001). The mean concentration of elastase was higher in patients (51.65 µg/mL; 95% CI: 44.47, 58.83 µg/mL) versus asymptomatic individuals (25.18 µg/mL; 95% CI: 21.64, 28.72 µg/mL; P = .001). The saliva cholesterol reported higher concentration in healthy individuals (3.43 mg/dL; 95% CI: 3.21, 3.65 mg/dL) compared to patients (1.16 mg/dL; 95% CI: 1.05, 1.27 mg/dL; P = .001). The saliva pH, and elastase concentration were significantly associated with the baseline RSS, while saliva cholesterol was negatively associated with the severity of RSS and RSA. CONCLUSION: Cholesterol, bile salts, and elastase are biomarkers of LPR and should be considered to develop future non-invasive saliva device for the detection of LPR.

Accuracy of ChatGPT-3.5 and -4 in providing scientific references in otolaryngology-head and neck surgery.

INTRODUCTION: Chatbot generative pre-trained transformer (ChatGPT) is a new artificial intelligence-powered language model of chatbot able to help otolaryngologists in practice and research. We investigated the accuracy of ChatGPT-3.5 and -4 in the referencing of manuscripts published in otolaryngology. METHODS: ChatGPT-3.5 and ChatGPT-4 were interrogated for providing references of the top-30 most cited papers in otolaryngology in the past 40 years including clinical guidelines and key studies that changed the practice. The responses were regenerated three times to assess the accuracy and stability of ChatGPT. ChatGPT-3.5 and ChatGPT-4 were compared for accuracy of reference and potential mistakes. RESULTS: The accuracy of ChatGPT-3.5 and ChatGPT-4.0 ranged from 47% to 60%, and 73% to 87%, respectively (p < 0.005). ChatGPT-3.5 provided 19 inaccurate references and invented 2 references throughout the regenerated questions. ChatGPT-4.0 provided 13 inaccurate references, while it proposed only one invented reference. The stability of responses throughout regenerated answers was mild (k = 0.238) and moderate (k = 0.408) for ChatGPT-3.5 and 4.0, respectively. CONCLUSIONS: ChatGPT-4.0 reported higher accuracy than the free-access version (3.5). False references were detected in both 3.5 and 4.0 versions. Practitioners need to be careful regarding the use of ChatGPT in the reach of some key reference when writing a report.

The Utilisation of Digital Applications for Measuring Patient Outcomes Following Bariatric Surgery: a Systematic Review and Meta-analysis of Comparative Studies.

In the context of escalating obesity rates, bariatric surgery holds a crucial role in managing severely obese patients. With a demonstrated effectiveness in weight loss and with the advent of ambulatory surgery, bariatric surgery allows for a streamlined care pathway, ideally suited for postoperative surveillance using digital health applications. The aim of this systematic review and meta-analysis is to evaluate the effect of eHealth-delivered health services or support for adults undergoing bariatric surgery. Five studies, encompassing 2210 patients, were analysed. The intervention group showed a 10% increase in total weight reduction and a 22% reduction in excess weight loss. ED visitation rates also trended towards reduction. Despite the absence of clear statistical superiority for DHA, the findings suggest potential benefits of DHA in postoperative monitoring.

Machine learning for risk stratification of thyroid cancer patients: a 15-year cohort study.

PURPOSE: The objective of this study was to train machine learning models for predicting the likelihood of recurrence in patients diagnosed with well-differentiated thyroid cancer. While thyroid cancer mortality remains low, the risk of recurrence is a significant concern. Identifying individual patient recurrence risk is crucial for guiding subsequent management and follow-ups. METHODS: In this prospective study, a cohort of 383 patients was observed for a minimum duration of 10 years within a 15-year timeframe. Thirteen clinicopathologic features were assessed to predict recurrence potential. Classic (K-nearest neighbors, support vector machines (SVM), tree-based models) and artificial neural networks (ANN) were trained on three distinct combinations of features: a data set with all features excluding American Thyroid Association (ATA) risk score (12 features), another with ATA risk alone, and a third with all features combined (13 features). 283 patients were allocated for the training process, and 100 patients were reserved for the validation of stage. RESULTS: The patients' mean age was 40.87 ± 15.13 years, with a majority being female (81%). When using the full data set for training, the models showed the following sensitivity, specificity and AUC, respectively: SVM (99.33%, 97.14%, 99.71), K-nearest neighbors (83%, 97.14%, 98.44), Decision Tree (87%, 100%, 99.35), Random Forest (99.66%, 94.28%, 99.38), ANN (96.6%, 95.71%, 99.64). Eliminating ATA risk data increased models specificity but decreased sensitivity. Conversely, training exclusively on ATA risk data had the opposite effect. CONCLUSIONS: Machine learning models, including classical and neural networks, efficiently stratify the risk of recurrence in patients with well-differentiated thyroid cancer. This can aid in tailoring treatment intensity and determining appropriate follow-up intervals.

How ChatGPT works: a mini review.

OBJECTIVE: This paper offers a mini-review of OpenAI's language model, ChatGPT, detailing its mechanisms, applications in healthcare, and comparisons with other large language models (LLMs). METHODS: The underlying technology of ChatGPT is outlined, focusing on its neural network architecture, training process, and the role of key elements such as input embedding, encoder, decoder, attention mechanism, and output projection. The advancements in GPT-4, including its capacity for internet connection and the integration of plugins for enhanced functionality are discussed. RESULTS: ChatGPT can generate creative, coherent, and contextually relevant sentences, making it a valuable tool in healthcare for patient engagement, medical education, and clinical decision support. Yet, like other LLMs, it has limitations, including a lack of common sense knowledge, a propensity for hallucination of facts, a restricted context window, and potential privacy concerns. CONCLUSION: Despite the limitations, LLMs like ChatGPT offer transformative possibilities for healthcare. With ongoing research in model interpretability, common-sense reasoning, and handling of longer context windows, their potential is vast. It is crucial for healthcare professionals to remain informed about these technologies and consider their ethical integration into practice.

Intensity and Network Structure of Insomnia Symptoms and the Role of Mental Health During the First Two Waves of the COVID-19 Pandemic.

Purpose: Insomnia, being a mental disorder, is best conceived as a network of symptoms. With the important increase in insomnia prevalence during the COVID-19 pandemic, our aim was to investigate how the structure of insomnia symptoms in the general population has changed due to the pandemic. We also looked at the directional dependencies of nightmares and of covid- and lockdown-related stress/anxiety and depression in insomnia. Patients and Methods: 5986 persons replied to our online questionnaire for the first wave and 2843 persons to our second wave questionnaire. Both questionnaires included the Insomnia Severity Index (ISI). Regularized Gaussian Graphical Models (GGM) and Bayesian Directed Acyclic Graphs (DAG) were estimated. Results: The pre- and peri-lockdown networks were equally strongly connected (first wave: S = 0.13, p = 0.39; second wave: S = 0.03, p = 0.67), but differed for the first lockdown regarding only six edges (M = 0.13, p < 0.001) and for the second lockdown only five edges (M = 0.16, p < 0.001). These symptoms all worsened during the lockdowns in comparison to before the pandemic (p < 0.001). The diurnal items of the ISI had the highest predictability and centrality values in the GGMs. Lockdown-related stress/anxiety influenced indirectly nightmares through covid-related stress/anxiety, lockdown-related depressive affect and mental fatigue. These reported feelings of stress/anxiety and depression showed an indirect impact on insomnia symptoms through mental and physical fatigue. Conclusion: Though the lockdown slightly intensified insomnia symptoms, it did not alter their network structure. Despite their differences, both GGMs and DAGs agree that the diurnal symptoms of the ISI, play an essential role in the network structure. Both methods confirm the need for emphasizing the cognitive/affective component in the treatment of insomnia (ie cognitive behavioral therapy).

Narrating: from Evolutionary to Clinical Advantages.

Human beings constantly narrate reality. They narrate themselves, to themselves and to others. They narrate each other and narrate humanity. They narrate the world and nature. They narrate meaning, the meaning of life and things. This article aims to explore this phenomenon of "narrating". Through a narrative review, we will attempt to gather elements of reflection on narrative, considered here as the ability to narrate, it means to represent oneself, to put meaning. Firstly, we will focus on how cognition, interpretation, and culture allow Homo Sapiens to narrate reality to himself. Then, we will explore why they do it and discover the evolutionary advantages of language, of sharing experiences between individuals through the phenomenon of cumulative cultural evolution, and how narrative facilitates the species' access to these advantages. Finally, we will delve into the clinical implications of narrative, discussing therapeutic interviews, therapy, and psychopathology. Narratives and pre-linguistic mental representations appear to have driven the Homo genus to develop cognitive abilities that enable the development of language and the sophistication of narratives as a cultural medium. Through language, Homo sapiens share their narratives and develop a cumulative common culture. Each individual's culture seems to be constructed in dialectic with this shared culture through narratives. This dialectic gives rise to psychopathological processes while also producing therapeutic leverage. Understanding the mechanisms of co-construction of these narratives is essential in clinical research in mental health. Furthermore, placing narratives in the perspective of an essential evolutionary strategy in the Homo genus solidifies the significance of the narrative faculty in the biological functioning of Homo sapiens, and so the importance of narratives in mental health.

Artificial Intelligence in Psychiatry.

Artificial Intelligence (AI) has emerged as a powerful tool in various fields, including psychiatry. This paper explores the potential of AI in the diagnosis, treatment, and understanding of psychiatric conditions. We delve into the role of AI in psychiatry, discussing its applications, challenges, and future directions. We explore how AI techniques such as classification, hypothesis generation, and prediction are being used in psychiatry, with a specific focus on the detection and prediction of psychiatric conditions. We also discuss the ethical considerations and challenges in implementing AI in psychiatry and look towards the future of AI in this field. The paper highlights the potential of AI to enhance our understanding of psychiatric conditions, improve patient care, and drive innovation in psychiatric research. However, it also underscores the need for robust ethical frameworks and stringent data protection measures to ensure the responsible and effective use of AI in psychiatry.

AI in Child Psychiatry: Exploring Future Tools for the Detection and Management of Mental Disorders in Children and Adolescents.

This article explores the potential of artificial intelligence (AI) in the field of child psychiatry, focusing on screening, detection, diagnosis, therapeutic tools, and research development. In this non-exhaustive review, we will examine the interest of AI applications in intervention contexts such as parent education tools, emotion regulation tools, and cognitive-behavioral therapy tools through interactive applications. The network approach, a branch of machine learning, will also be considered. Some examples of machine learning applications in child psychiatry will be presented. Finally, we address the ethical question of the role of these applications, analyzing whether they represent a real beneficial tool or a potential danger.

Neuropsychiatry and Neuropsychopharmacology of Dementia: a Guide to Evaluation, Diagnosis, and Management.

In the present study, I provide an examination of the neuropsychiatric approach to patients with various types of dementia, including Alzheimer's disease, Parkinson's disease dementia, Lewy body dementia, vascular dementia, frontotemporal dementia, and more. With a focus on the intersection of psychiatry and neurology, this paper underscores the importance of comprehensive neuropsychiatric evaluation, rigorous diagnosis, and evidence-based management. The paper delineates the neuropsychiatric manifestations specific to each type of dementia and explores both non-pharmacological and pharmacological management strategies, aiming to equip psychiatrists with the latest evidence-based approaches. Case studies are included to demonstrate real-world clinical scenarios and to provide insights into the practical application of the theories discussed. Additionally, this guide addresses current challenges in the neuropsychiatric approach to dementia and highlights potential solutions and future research directions. The primary objective of this guide is to enable psychiatrists to enhance the quality of life for individuals living with dementia by improving understanding, diagnosis, and management of the neuropsychiatric aspects of these conditions.

Somatic Management of Psychiatric Patients in the Emergency Department.

This retrospective study aims to assess a potential difference in the management of patients with a psychiatric history in somatic emergencies. Indeed, the psychiatric population has higher mortality and morbidity rates than the general population. The negative stigmatization of patients with mental health disorders remains one of the factors to consider when studying this morbidity and mortality. In this context, adult patients diagnosed with myocardial infarction, pulmonary embolism, stroke, acute cholecystitis or appendicitis in the emergency department of the Brugmann University Hospital Center during the year 2021 were selected. The presence or absence of a history psychiatric was then recorded for each patient. Different key intervention times, the total length of stay and the occurrence of complications were also studied for 459 patients, 74 of which had a history psychiatric. A significant difference in the time preceding the prescription of the first complementary examination for patients with a psychiatric history was thus highlighted. No other differences in care were demonstrated within the limits of this sample. This difference could be associated with the phenomenon of diagnostic overshadowing. It is the fact of associating the somatic complaints of a patient with his psychiatric pathology. Another potential explanation, present in the literature, could be the discomfort felt by somaticians when dealing with psychiatric patients. Finally, the integration of the experience of psychiatric patients into the training of physicians and the question of the relevance of applying the triage system to psychiatric patients were raised as potential future studies.

Empathy and Psychopathy Traits in University Students in Belgium: a Network Approach.

Empathy is the complex prosocial cognitive capacity to recognize and react to the emotions of others. An empathic attitude from medical doctors is essential to build a good relationship with patients. In engineering education there is an hypervalorization of technical skills in disadvantage of these affective elements. Psychopathy is the lack of considerations toward others. These two important personality traits shape social interactions. In this study we analyzed, through the network theory, these characteristics in a young population of medical and engineering university students in Belgium. The aim of this study was therefore to estimate the individual network structure of these traits in both groups, as well as estimate whether there is a fundamental difference in the way that these traits connect in these two populations. Medical and engineering students completed online three self-report questionnaires about empathy and psychopathy traits. There were 178 responders without exclusions due outliers. No significant differences were found in psychopathic traits between the two groups. There was a statistically significant difference in empathic concerns, the medical students being more empathic than their peers in engineering. Psychopathic traits did not vary significantly between the two groups. This study provided insights into the differences in empathic and psychopathic traits among those students. Future research should explore the factors that contribute to these differences and investigate the potential impact of targeted interventions or curricular modifications in cultivating empathy and minimizing antisocial behaviors in both fields.

Prevalence of Insomnia and Sleep Habits during the First and Second Wave of COVID-19 in Belgium.

Belgium has one of the highest numbers of COVID-19 cases per 1 million inhabitants. The pandemic has led to significant societal changes with repercussions on sleep and on mental health. We aimed to investigate the effect of the first and the second wave of COVID-19 on the sleep of the Belgian populationWe launched two online questionnaires, one during the first lockdown (7240 respondents) and one during the second (3240 respondents), to test differences in self-reported clinical insomnia (as measured by the Insomnia Severity Index) and sleep habits during the two lockdowns in comparison with the pre-COVID period. The number of persons with clinical insomnia rose during the first lockdown (19.22%) and further during the second (28.91%) in comparison with pre-lockdown (7.04-7.66%). Bed and rise times were delayed and there was an increased time in bed and sleep onset latency. There was further a decrease in total sleep time and in sleep efficiency during both confinements. The prevalence of clinical insomnia quadrupled during the second wave in comparison with the pre-lockdown situation. Sleep habits were most altered in the younger population, indicating a greater risk for this group to develop a sleep-wake rhythm disorder.

Online software Boneureka assessing bone age based on metacarpal length in healthy children: proof-of-concept study.

BACKGROUND: Bone age in children is mainly assessed using the Greulich and Pyle (GP) atlas, a validated method with limited interobserver accuracy. While automated methods increase interobserver accuracy, they represent considerable costs and technical requirements. OBJECTIVE: A proof-of-concept study to create and evaluate an online software program, Boneureka©, based on linear metacarpal length measurements, to assess bone age in healthy children. MATERIALS AND METHODS: The study retrospectively included 434 consecutive children (215 girls) who underwent a left-hand radiograph to rule out trauma between March 2008 and December 2017. Two reviewers measured the second to fourth metacarpal lengths on each radiograph and the distance between the centre of the epiphyses of the second and fifth metacarpals. A single reviewer estimated the bone age using the GP atlas. The automated software assessed the bone age for all radiographs. A mathematical model was developed based on linear regressions to provide the mean bone age and standard deviation based on the estimates. Pearson and intraclass correlation coefficient (ICC) were used to evaluate the correlation and agreement between the estimated bone ages using Boneureka©, the GP atlas and BoneXpert® compared to chronological age. RESULTS: The measure that showed the highest correlation (r2=0.877 for girls and r2=0.834 for boys; P<.001) and the highest ICC (ICC=0.937 for girls and ICC=0.926 for boys; P<0.001) with chronological age was length of the second metacarpal. The GP atlas and the automated software evaluation had excellent ICC with chronological age (ICC>0.95 for both methods and sexes). Using this data, we created an online software program based on the second metacarpal length to obtain bone age estimates, means and standard deviations. CONCLUSION: The newly created online software Boneureka,© based on the second metacarpal length, is a reliable and user-friendly tool to assess bone age in healthy children. Further studies on a larger population should be performed to validate the developed reference values.

A tutorial on bayesian networks for psychopathology researchers.

Bayesian Networks are probabilistic graphical models that represent conditional independence relationships among variables as a directed acyclic graph (DAG), where edges can be interpreted as causal effects connecting one causal symptom to an effect symptom. These models can help overcome one of the key limitations of partial correlation networks whose edges are undirected. This tutorial aims to introduce Bayesian Networks to identify admissible causal relationships in cross-sectional data, as well as how to estimate these models in R through three algorithm families with an empirical example data set of depressive symptoms. In addition, we discuss common problems and questions related to Bayesian networks. We recommend Bayesian networks be investigated to gain causal insight in psychological data. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A network perspective on abnormal child behavior in primary school students.

Abnormal child behavior is a complex entity including among others disorders such as ADHD, a neuropsychiatric disorder interfering with the normal development of the child, and combining many comorbidities. There are no specific tools designed to assess abnormal child behavior, although well-known ADHD screening tools such as the CTRS-R:S go beyond the disorder itself and cover the wider construct. This can be understood as the complex system of mutually influencing entities, that is why the aim of this study was to investigate the items of the CTRS-R:S as a psychological network. We analyzed a data set of 525 children in French-speaking primary schools from Belgium, and estimated a Bayesian Gaussian Graphical Model with a Bayes Factor of 30. We identified highly connected nodes from each community of the CTRS-R:S to build an additional network of communities. In the domain network, Oppositional and Cognitive skills share the strongest connection in the network. The 28-item network was overall positively connected albeit the presence of some negative connections. The spinglass algorithm identified eight domains in the CTRS-R:S network that causally influence each other. The network approach is a useful framework to investigate the domains of abnormal child behavior as well as the interplay between individual symptoms.

Using Bayesian Networks to Investigate Psychological Constructs: The Case of Empathy.

Network analysis is an emerging field for the study of psychopathology that considers constructs as arising from the interactions among their constituents. Pairwise effects among psychological components are often investigated by using this framework. Few studies have applied Bayesian networks, models that include directed interactions to perform causal inference on psychological constructs. Directed graphical models may be less straightforward to interpret in case the construct at hand does not contain symptoms but instead psychometric items from self-report measures. However, they may be useful in validating specific research questions that arise while using standard pairwise network models. In this study, we use Bayesian networks to investigate a well-known psychological construct, empathy from the Interpersonal Reactivity Index, in large two samples of 1973 university students from Belgium. Overall, our results support the hypotheses emphasizing empathic concern (i.e., sympathy) as causally important in the construct of empathy, and overall attribute the primacy of emotional components of empathy over their intellectual counterparts. Bayesian networks help researchers identify the plausible causal relationships in psychometric data, to gain new insight on the psychological construct under examination, help generate new hypotheses and provide evidence relevant to old ones.