Description Of Symptoms Research Articles

Analysis of Work-Related Stress Among Nurses in the Inpatient Ward of RSUD So'e: A Description of Physiological, Psychological, and Behavioral Symptoms

Introduction: Work stress is a significant issue that can adversely impact both physical and mental health, particularly among healthcare professionals like nurses. It often arises from high patient demands, heavy workloads, and emergency situations requiring quick responses. Objective: This study aims to describe the level of work stress experienced by nurses in the inpatient ward of So'e Regional General Hospital and to identify the associated symptoms. Method: The research employed a descriptive design with a quantitative approach, utilizing a case study format. A structured questionnaire was administered to collect data, involving 115 respondents who work in the inpatient ward. Result: The findings revealed that the majority of nurses experienced work stress categorized as mild, with only a small percentage reporting moderate or severe stress levels. Additionally, the data indicated that the prevalence of physiological, psychological, and behavioral symptoms among the respondents was generally low, suggesting that while stress is present, it may not be at a level that significantly affects their overall health. Conclusion: This study underscores the importance of understanding work stress conditions among nurses. The results provide a foundational overview that can inform the development of targeted stress management interventions in healthcare settings. By addressing these issues, hospitals can improve nurse well-being and enhance the overall quality of healthcare services, ultimately benefiting both staff and patients.

Exploring the experiences of traditional practitioners while managing hypertension in Rwanda

BackgroundHypertension (HTN) is a major global health concern, affecting approximately 1.28 billion adults worldwide. In Rwanda, hypertension contribute to 2.81 of the disability adjusted life years (DALYs) and 1.87% of hypertensive heart disease mortality. Traditional medicine remains popular in Africa with some herbal remedies which have shown antihypertensive properties. However, no study was done to explore traditional Practitioners’ approach to hypertension in Rwanda.ObjectiveTo explore the experience of traditional Practitioners while managing hypertension in Rwanda.MethodologyThis qualitative study utilized an ethnographic approach to explore the experience of traditional Practitioners (TPs) while managing HTN in Rwanda. Semi- structure interviews were conducted with 20 traditional Practitioners from three districts known for their popular use of traditional medicine in Rwanda. Participants were purposively selected, and interviews were guided by a pretested semi-structured interview guide in local language. Thematic analysis was used to generate the key themes.ResultsThree main themes emerged from analysis: first, traditional medicine is often undervalued by modern medical providers, resulting in limited collaboration and communication between traditional Practitioners and modern medical providers. Despite this, TPs are trusted by the community, often receiving referrals from patients dissatisfied with modern treatment. Second, TPs face challenges in diagnosing HTN due to lack of diagnostic tools, often relying on their observation, patients’ description of symptoms and patient’s response to treatment. TPs expressed desire for collaboration with modern medicine and access to diagnostic tools. Third, lack of legal framework and standardized practices for traditional medicine remains a challenge.ConclusionThe study highlights the need to promote collaboration between traditional and modern medicine in Rwanda. To enhance HTN management, we recommend training of traditional Practitioners, setting standardized practice and legal framework to guide traditional Practitioners in Rwanda as well as improving their access to HTN diagnostic tools.

Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure

Serial functional status assessments are critical to heart failure (HF) management but are often described narratively in documentation, limiting their use in quality improvement or patient selection for clinical trials. To develop and validate a deep learning natural language processing (NLP) strategy for extracting functional status assessments from unstructured clinical documentation. This diagnostic study used electronic health record data collected from January 1, 2013, through June 30, 2022, from patients diagnosed with HF seeking outpatient care within 3 large practice networks in Connecticut (Yale New Haven Hospital [YNHH], Northeast Medical Group [NMG], and Greenwich Hospital [GH]). Expert-annotated notes were used for NLP model development and validation. Data were analyzed from February to April 2024. Development and validation of NLP models to detect explicit New York Heart Association (NYHA) classification, HF symptoms during activity or rest, and frequency of functional status assessments. Outcomes of interest were model performance metrics, including area under the receiver operating characteristic curve (AUROC), and frequency of NYHA class documentation and HF symptom descriptions in unannotated notes. This study included 34 070 patients with HF (mean [SD] age 76.1 [12.6] years; 17 728 [52.0]% female). Among 3000 expert-annotated notes (2000 from YNHH and 500 each from NMG and GH), 374 notes (12.4%) mentioned NYHA class and 1190 notes (39.7%) described HF symptoms. The NYHA class detection model achieved a class-weighted AUROC of 0.99 (95% CI, 0.98-1.00) at YNHH, the development site. At the 2 validation sites, NMG and GH, the model achieved class-weighted AUROCs of 0.98 (95% CI, 0.96-1.00) and 0.98 (95% CI, 0.92-1.00), respectively. The model for detecting activity- or rest-related symptoms achieved an AUROC of 0.94 (95% CI, 0.89-0.98) at YNHH, 0.94 (95% CI, 0.91-0.97) at NMG, and 0.95 (95% CI, 0.92-0.99) at GH. Deploying the NYHA model among 182 308 unannotated notes from the 3 sites identified 23 830 (13.1%) notes with NYHA mentions, specifically 10 913 notes (6.0%) with class I, 12 034 notes (6.6%) with classes II or III, and 883 notes (0.5%) with class IV. An additional 19 730 encounters (10.8%) could be classified into functional status groups based on activity- or rest-related symptoms, resulting in a total of 43 560 medical notes (23.9%) categorized by NYHA, an 83% increase compared with explicit mentions alone. In this diagnostic study of 34 070 patients with HF, the NLP approach accurately extracted a patient's NYHA symptom class and activity- or rest-related HF symptoms from clinical notes, enhancing the ability to track optimal care delivery and identify patients eligible for clinical trial participation from unstructured documentation.

“I think we should wait and see”: A qualitative study of call-takers’ decision-making in consultations with patients suffering unrecognized myocardial infarction

ObjectivesCall-takers face a complex situation when assessing medical problems in emergency medical services calls. Patients with myocardial infarction experiencing atypical symptoms risk misinterpretation. We examined development in call-takers' decision-making process in telephone consultations with patients having imminent myocardial infarction. MethodsRecording of 38 calls among 19 patients (two per patient) who contacted Copenhagen Emergency Medical Services (Denmark) at least twice within one week before myocardial infarction diagnosis. The penultimate and last call were compared using qualitative content analysis. ResultsCall-takers’ assessment of the condition changed from symptom picture and dismissal of heart disease in penultimate call to severe condition, not heart-related, and possible heart disease in last call. Call-takers recommended watchful waiting in the penultimate call. Both calls involved response negotiation, while caution regarding misinterpretation was only seen in the penultimate call. ConclusionCall-takers used different decision-making approaches when the caller’s symptom descriptions appeared unclear and not corresponding with the medical understanding of severe conditions. Call-takers did not negotiate the condition's assessment but engaged in discussions about the response choice. Practice implicationsA protocol to negotiate response choice with callers having unclear clinical conditions should be developed. Clarifying watchful waiting as a recommendation may assist call-takers’ decision-making.

ClinicAl verSus algorIthmic predictioN of Obstructive Coronary Artery Disease - the CASINO study

Abstract Background The estimation of pretest probability (PTP) is a key step when evaluating patients with suspected coronary artery disease (CAD). Current European guidelines recommend using an algorithm based on age, sex, and symptom typicality. However, many cardiologists do not regularly use this method, relying instead on overall clinical impression. The aim of this study was to compare clinical and algorithmic prediction of obstructive CAD in a set of symptomatic patients with suspected chronic coronary syndrome (CCS). Methods In this survey study, 10 cardiologists from were asked to estimate the probability of obstructive CAD (on a scale of 1-99% and without the aid of any score or algorithm) for 120 anonymized clinical vignettes of outpatients who underwent diagnostic workup for suspected CCS. The provided information included age, sex, symptom description, cardiovascular risk factors, and know comorbidities. Whenever non-invasive tests had been performed, physicians were also asked to estimate the probability of obstructive CAD after knowing that results. Obstructive CAD was defined as diameter stenosis ≥50% confirmed by invasive coronary angiography. Results Among the 120 included patients (62 women, mean age 61±10 years), symptoms consisted of chest pain in 104 and dyspnea in the remainder. The median PTP of obstructive CAD was 15% (IQR 10-25%) according to the ESC algorithm, and 30% (IQR 15-50%) according to cardiologists’ estimates (p<0.001). The observed prevalence of obstructive CAD was 20% (n=24), yielding suitable calibration for the ESC model (predicted vs. observed p=0.631), and significant overestimation for clinicians’ assessments (predicted vs. observed p=0.014) – Figure 1A. Despite using the same data, cardiologists’ PTP estimates varied widely within the same patients (mean inter-observer coefficient of variation 56%±22%). In a subset of 29 patients whose PTP was appraised twice by the same physicians, the mean absolute difference between assessments (intra-observer variability) was 22%±8%. The ESC algorithm showed fair discriminative power to identify patients with obstructive CAD (C-statistic 0.68, 95% CI 0.58-0.79, p=0.005), whereas the C-statistic for clinicians ranged from 0.53 to 0.72 (average 0.62±0.05, p values for comparisons 0.001-0.677) – Fig 1B. In the subset of 70 patients with prior non-invasive tests, clinicians significantly changed their predictions after knowing test results (mean absolute difference 22%±12%, p<0.001), improving their discriminative power significantly (mean change in C-statistic of 0.08±0.09, p=0.020). Conclusion Clinicians’ assessments of PTP in symptomatic patients display high inter- and intra-observer variability, and tend to overestimate the likelihood of obstructive CAD. Despite using more clinical data, cardiologists fail to outperform the ESC algorithm. The systematic use of this tool should be promoted in order to improve disease prediction and guide non-invasive testing.

Symptom assessment and angina classification of patients with ischemia and nonobstructive coronary arteries undergoing invasive coronary function testing: from the DISCOVER INOCA multicenter registry

Abstract Background Patients with ischemia and nonobstructive coronary arteries (INOCA) suffer from a broad range of anginal symptoms that may differ depending on the biologic sex of the patient, the underlying coronary vasomotor disorder, or the type of presentation. Purpose We sought to characterize the symptom description and angina classification of patients referred for invasive coronary function testing (CFT) in the DISCOVER INOCA multicenter registry, stratified by type of presentation (stable vs unstable), biologic sex, and underlying diagnosis, including coronary microvascular dysfunction (CMD), vasospastic angina (VSA), or other disorders such as symptomatic myocardial bridging. Methods DISCOVER INOCA is a prospective, multicenter registry in the United States that includes patients who are referred for clinically indicated coronary angiography and found to have INOCA. All patients underwent protocol-guided angiography, acetylcholine provocation, coronary thermodilution, and intravascular imaging. Symptom characteristics were compared between groups. Results From September 2022 until February 2024, 104 patients were enrolled, 78% were female, and mean age was 57 ± 11 years. The post-procedure diagnosis was CMD in 19%, VSA in 41%, a mixed syndrome in 15%, or other diagnosis in 26%. The average coronary flow reserve (CFR) was 2.9 ± 1.1 in the CMD group and 5.3 ± 3.0 in the VSA group, (p < 0.001). 53% of patients presented with stable angina and 47% with unstable or progressive symptoms. Symptoms included chest pain (89%), chest tightness (42%), chest pressure (39%), shortness of breath (53%), and fatigue (16%), and no significant differences were reported when stratified by diagnostic phenotype. Female patients were more likely to report arm/neck/back/jaw pain (25% vs 5% in men, p = 0.038). Symptom triggers included exertion/activity in 73%, emotional stress in 12%, and resting symptoms in 58%. In female patients 4% reported catamenial symptoms. Of patients with stable angina, the Canadian Cardiovascular Society (CCS) Angina Classification was Grade I in 25%, Grade II in 52%, Grade III in 21%, and Grade IV in 2%. In patients with unstable angina, the Braunwald Angina Classification was Class I in 9%, Class II in 73%, and Class III in 18%. Conclusions Patients with INOCA referred for clinically indicated coronary angiography and CFT were predominantly female and most commonly reported chest pain as a presenting symptom. Female patients were more likely to report arm/neck/back/jaw pain than male patients. Over half of patients reported resting symptoms with no differences between the diagnostic phenotypes (CMD, VSA, or other). The CCS and Braunwald angina classification indicated that most patients had moderate intensity symptoms. Patient-reported outcome measures will be used to systematically categorize the symptom burden in these patients.

09 Penicillin allergy de-labeling: Adaptation of risk stratification tool for patients and families

Abstract Background Penicillin allergy is reported in 10% of the population, however, over 90% of patients are deemed non-allergic upon allergist assessment. A validated mobile penicillin allergy risk assessment tool was previously developed to help healthcare providers identify patients at low risk of penicillin allergy and de-label them. Objectives The goal of this quality improvement project is to validate a patient-driven version of the assessment tool. Design/Methods Patients 6 months to 18 years old and pregnant women referred to the institution’s allergy clinics for penicillin allergy assessment were invited to use the patient tool to complete a self-assessment prior to their appointment, resulting in the assignment of a risk category: allergic, high risk, low risk, or not allergic. At their appointment, the allergist conducted an assessment with the standardized validated tool. The risk categories determined using the patient tool were compared against the allergist assessment. Patients were provided satisfaction surveys. Results The allergist assessment and patient tool demonstrated agreement in 57/84 (67.9%, 95% CI [56.7%,77.4%]) assessments, intra-class correlation (ICC) = 0.618, p<0.001. Of these, 45/57 (78.9%) patients were stratified as low risk or not allergic. In 22/84 (26.2%) assessments, the patient tool determined a higher risk category than the allergist, primarily due to differences in patients’ perceived timing and description of symptoms. Only 5/84 (6.0%) patients were placed in a lower risk category using the patient tool compared to the validated tool. Of these, one patient responded to the self-assessment accounting for a mild reaction, whereas the allergist assessment accounted for a second, more severe reaction. The other 4/5 patients mistakenly answered that they had never taken penicillin so were stratified as not allergic on the patient tool; however, they had previously taken penicillin and were found at risk of possible allergy by the allergist. Of those who responded to the satisfaction survey, 53/62 (85.5%) agreed that the patient tool was easy to use. Conclusion The patient tool demonstrates good agreement in determining penicillin allergy risk, generally aligning with the validated tool and offering potential as a method of empowering patients to assess their risk and advocate for their care. For a small minority of patients, the patient tool determined a lower risk category than the allergist assessment. To improve safety, agreement, and accessibility, iterative cycles of assessment and changes to the patient tool structure will be applied. Potential competing interests Funding was obtained from Valeo, Polaris Health, CeraVe, ALK, Astra Zeneca, Medexus, Novartis, Sanofi, Pfizer

Diary method of studying the stress factors in adolescents in the educational process of the school

The article is devoted to the current problem of the growth of psychosomatic disorders in adolescents. The purpose of the article is to determine the socio-psychological stress factors that cause a psychosomatic symptom in adolescents in the educational process of school using the method of diary research. The authors present the results of a three-week experiment conducted with students in grades 9–10, as a result of which records were obtained about specific stressful situations with descriptions of emotions, physical sensations and psychosomatic symptoms. The analysis of the data obtained was carried out, during which the authors established the similarity between the described situations in some participants of the experiment, who were later grouped and displayed in a table. The article uses mathematical calculations to determine the percentage of schoolchildren who are faced with one or another stress factor. The study helped identify a number of problems, including those related to parents' ignorance and lack of knowledge in the field of children's mental health. The authors drew conclusions about the importance of continuing research in this direction in connection with the trend of growth of psychosomatic disorders in adolescents and proposed ideas for an optimal solution to the described problem.

Assessing the Capability of Advanced AI Models in Cardiovascular Symptom Recognition: A Comparative Study

The field of medical informatics has been significantly transformed in recent years with the emergence of Natural Language Understanding (NLU) and Large Language Models (LLM), providing new opportunities for innovative patient care solutions. This study aims to evaluate the effectiveness of publicly available LLMs as symptom checkers for cardiological diseases by comparing their diagnostic capabilities in real disease cases. We employed a set of 9 models, including ChatGPT-4, OpenSource models, Google PaLM 2, and Meta’s LLaMA, to assess their diagnostic accuracy, reliability, and safety across various clinical scenarios. Our methodology involved presenting these LLMs with symptom descriptions and test results in Spanish, requiring them to provide specialist diagnoses and recommendations in English. This approach allowed us to compare the performance of each model, highlighting their respective strengths and limitations in a healthcare context. The results revealed varying levels of accuracy, precision, and sensitivity among the models, demonstrating the potential of LLMs to enhance medical education and patient care. By analysing the capabilities of each model, our study contributes to a deeper understanding of artificial intelligence’s role in medical diagnosis. We argue for the strategic implementation of LLMs in healthcare, emphasizing the importance of balancing sensitivity and realism to optimize patient outcomes.

A Hybrid System Based on Bayesian Networks and Deep Learning for Explainable Mental Health Diagnosis

Mental illnesses are becoming one of the most common health concerns among the population. Despite the proven efficacy of psychological treatments, mental illnesses are largely underdiagnosed, particularly in developing countries. A key factor contributing to this is the scarcity of mental health providers capable of diagnosing. In this work, we propose a novel method that combines the general capabilities and accuracy of Large Language models with the explainability of Bayesian Networks. Our system analyzes descriptions of symptoms provided by users and written in natural language and, based on these descriptions, asks questions to confirm or refine the initial diagnosis made by the deep learning model. We trained our model on a large-scale dataset collected from various internet sources, comprising over 2.3 million data points. The initial prediction from the Large Language model is refined through symptom confirmation questions derived from a probabilistic graphical model constructed by experts based on the DSM-5 diagnostic manual. We present results from symptom descriptions sourced from the internet and clinical vignettes extracted from behavioral science exams, demonstrating the effectiveness of our hybrid model in classifying mental health disorders. Our model achieves high accuracy in classifying a wide range of mental health disorders, providing transparent and explainable predictions.

Revealing the Causes of Dyslexia through a Differential Diagnosis, a Short-Term Effective Treatment and an Appropriate Conceptual Framework.

Various different impairments and their interactions can cause reading problems referred to as "dyslexia". Since reading requires the interaction of many abilities, the impairment of each of these abilities can result in dyslexia. Therefore, the diagnosis must differentiate various kinds of dyslexia. The diagnosis of a certain kind of dyslexia cannot be delimited to the investigation and description of symptoms but must also include the investigation of the causes of each kind of dyslexia. For this purpose, a scientifically unequivocal concept of causation and appropriate methods are needed to distinguish them from co-existing impairments that have no causal influence on reading performance. The results of applying these methods cannot be adequately accounted for by a non-scientific, intuitive understanding of necessary and sufficient conditions and causation. The methods suitable for revealing the causes of dyslexia are described in detail, and the results of applying these methods in experiments, in which 356 children with developmental dyslexia participated, are reviewed. Since the concepts of "necessary" and "sufficient" conditions and "causation" proposed in the philosophy of science are not suitable for describing causes of dyslexia and their interaction, they are replaced by a more detailed, experimentally based conceptual framework that provides an accurate description of the conditions required for correct reading and the causes of dyslexia.

Beyond seen faults: Zero-shot diagnosis of power circuit breakers using symptom description transfer

Power circuit breakers (CBs) are vital for the control and protection of power systems, yet diagnosing their faults accurately remains a challenge due to the diversity of fault types and the complexity of their structures. Traditional data-driven methods, although effective, require extensive labeled data for each fault class, limiting their applicability in real-world scenarios where many faults are unseen. This paper addresses these limitations by introducing symptom description transfer-based zero-shot fault diagnosis (SDT-ZSFD), a method that leverages zero-shot learning for fault diagnosis. Our approach constructs a fault symptom description (FSD) framework, which embeds a fault symptom layer between the feature layer and the label layer to facilitate knowledge transfer from seen to unseen fault classes. The method utilizes current and acceleration signals collected during CB operation to extract features. By applying sparse principal component analysis to these signals, we derive high-quality features that are mapped to the FSD framework, enabling effective zero-shot learning. Our method achieves a satisfactory recognition rate by accurately diagnosing unseen faults based on these symptoms. This approach not only overcomes the data scarcity problem but also holds potential for practical applications in power system maintenance. The SDT-ZSFD method offers a reliable solution for CB fault diagnosis and provides a foundation for future improvements in symptom-based zero-shot diagnostic mechanisms and algorithmic robustness.

Inconsistent decision making in dental caries diagnosis and treatment: A case-based questionnaire survey.

Delayed implementation of new knowledge into clinical practice poses patient safety risks. This study investigates agreement on use of the dental caries interventions, sealing, and stepwise excavation. A cross-sectional questionnaire survey, based on 11 constructed cases with descriptions of patient symptoms, radiographic, and clinical findings. Interrater agreement on dental caries- and pulp diagnoses and interventions were measured with Cohen's and Light's κ. The data collection period was September 28 to November 5, 2021. To explore variations in use and knowledge factors, we examined Danish dentists' attitudes towardcontinuing education. Based on 243 responses, moderate interrater agreement for dental caries and pulp diagnoses and weak agreement on interventions were seen. The agreement with the gold standard for caries was moderate. No agreement was found for dental pulp diagnosis, and for interventions the agreement was weak. No pattern in agreement with the gold standard was seen in relation to case difficulty level. The majority reported knowing of and using stepwise excavation, in conflict with findings that less than half chose stepwise excavation in cases, where considered appropriate. One in four (25%) reported to be unfamiliar with sealing, and half (50%) use sealing regularly. Better access to continuing education and for universities to offer continuing education as alternatives to one-sidedprivate market were requested. Some patients may receive too radical treatment despite available less invasive evidence-based effective treatments. Dentists acknowledge the importance of continuing education. Easier access and perhaps more incentives for seeking out high-quality continuing education from trustworthy sources are needed.

The most impactful endometriosis symptom: An international, cross-sectional, two-round survey study.

There is considerable variation in the types of symptoms experienced by people living with endometriosis, and it is unclear which symptoms impact people the most. This study aimed to identify the specific symptoms that are "most impactful" to people living with the condition. Two sequential online surveys were conducted. Women aged over 18 years with a diagnosis of endometriosis were eligible to participate. Participants first provided a free-text list of all the endometriosis symptoms they experienced (Survey 1, Australian only). Responses were condensed into a shorter list by grouping symptom types and selecting the top 20 most common and most impactful. Survey 2 (international) participants reviewed the list and selected all that they had experienced in the last 3 months, nominated one as their single "most impactful symptom", and rated its impact on one of five randomized scale types. Survey 1 and Survey 2 had 195 and 983 responses, respectively. The mean age of respondents was 30.8 ± 7.9 years. There were 275 separate symptom descriptions from Survey 1, which were condensed into 104 groups, of which 25 met criteria for inclusion in Survey 2. The most commonly experienced symptoms were abdominal pain (93% of respondents), bloating (92%), and fatigue (90%), and the symptoms nominated as causing the most impact were pelvic pain (20%), abdominal pain (15%), and cramps (7%). Nearly everyone (99.7%) in Survey 2 reported experiencing at least one pain symptom. The symptoms that generated the highest impact scores were infertility (99.8/100), irregular menstrual cycles (95.3/100), and constipation (92/100). The average impact score was 87.5/100. There was substantial variation in the symptom selected as causing the most impact, and the level of impact was high. A focus on measuring the "most impactful symptom" in future research may enable us to better capture and measure the true symptom experience.

Multifaceted approach to successful management of pediatric acute liver failure: High-volume plasma exchange as a pivotal intervention

Abstract: Pediatric acute liver failure (pALF) poses a formidable clinical challenge characterized by rapid hepatocyte necrosis and pro-inflammatory events. The urgency of pALF necessitates advanced therapeutic modalities, prompting the exploration of high-volume plasma exchange (HVPE) as a sophisticated intervention. This case report delves into the intricate management of a 16-year-old female with hyperacute liver failure secondary to hepatitis A, highlighting the critical need for nuanced and multidisciplinary approaches. The case unfolds through a detailed description of symptoms, clinical decision-making, and the therapeutic journey. The management includes a calculated deferral of liver transplantation in favor of HVPE, with a focus on the rationale for choosing HVPE over continuous renal replacement therapy. Technical aspects of the procedure, such as blood volume calculations and procedural details, are meticulously outlined. Laboratory investigations track the patient’s progress, showcasing the profound impact of HVPE on clinical and laboratory recovery. The patient’s response to HVPE is evident in the comprehensive clinical and laboratory recovery observed after the intervention. The resolution of encephalopathy, successful extubation, and eventual discharge highlight the positive outcomes achieved through the judicious application of HVPE. However, the procedure is not without challenges, as the emergence of hypertension during the second and third cycles and a lone episode of seizure require swift and careful management. The management of pALF demands a sophisticated and multidimensional approach, especially when liver transplantation is not immediately feasible. This case report underscores the nuanced decision-making involved in selecting extracorporeal therapies, with a focus on the successful application of HVPE. HVPE, as recommended by the American Society for Apheresis, emerges as a promising intervention in the context of pALF, offering a viable option when liver transplantation is deferred due to advanced encephalopathy. The favorable outcomes and considerations of cost-effectiveness and procedural simplicity further accentuate the potential of HVPE as a cornerstone in the critical care of pediatric patients with acute liver failure.

ChatGPT as an Information Source for Patients with Migraines: A Qualitative Case Study.

Migraines are one of the most common and expensive neurological diseases worldwide. Non-pharmacological and digitally delivered treatment options have long been used in the treatment of migraines. For instance, migraine management tools, online migraine diagnosis or digitally networked patients have been used. Recently, applications of ChatGPT are used in fields of healthcare ranging from identifying potential research topics to assisting professionals in clinical diagnosis and helping patients in managing their health. Despite advances in migraine management, only a minority of patients are adequately informed and treated. It is important to provide these patients with information to help them manage the symptoms and their daily activities. The primary aim of this case study was to examine the appropriateness of ChatGPT to handle symptom descriptions responsibly, suggest supplementary assistance from credible sources, provide valuable perspectives on treatment options, and exhibit potential influences on daily life for patients with migraines. Using a deductive, qualitative study, ten interactions with ChatGPT on different migraine types were analyzed through semi-structured interviews. ChatGPT provided relevant information aligned with common scientific patient resources. Responses were generally intelligible and situationally appropriate, providing personalized insights despite occasional discrepancies in interaction. ChatGPT's empathetic tone and linguistic clarity encouraged user engagement. However, source citations were found to be inconsistent and, in some cases, not comprehensible, which affected the overall comprehensibility of the information. ChatGPT might be promising for patients seeking information on migraine conditions. Its user-specific responses demonstrate potential benefits over static web-based sources. However, reproducibility and accuracy issues highlight the need for digital health literacy. The findings underscore the necessity for continuously evaluating AI systems and their broader societal implications in health communication.

Reliability and validity of the German version of the Glasgow Edinburgh Throat Scale

Many patients who consult the ENT doctor suffer from globus sensation, as a term for a multifactorial symptom description. It is particularly important during initial consultation to take the often-sensitive patients seriously and to offer a structured diagnostic clarification. Today, there is a lack of a screening tool in everyday clinical practice that can be used to differentiate the globus sensations more closely and assess their severity OBJECTIVES: The reliability and validity of the German version of the Glasgow Edinburgh Throat Scale (GETS) were examined. The original GETS questionnaire was translated into German (GETS-G) and completed by 60 patients with globus sensation (>3 months) in a prospective observational study. All patients received a clinical examination a sonography of the neck. In patients without an identifiable organic cause, a pH monitoring was carried out. A local organic cause for the globus sensation was found in 30 patients (50%). In addition, reflux was detected in 24 patients (40%) using pH monitoring. No cause could be determined in only 6 patients (10%). According to the overall score (Q1-Q12), there was no significant difference between these three groups. The Cronbach's alpha for assessing reliability was 0.88 in the 12-item analysis. Principal component analysis yielded a globus scale, dysphagia scale and a chronic pharyngeal irritation scale. The GETS-G showed high reliability and validity, which justifies its use in patients with globus sensation in German-speaking countries and may lead to a better assessment of the severity of this symptom.

The Interrelationships between Cytokines and Schizophrenia: A Systematic Review.

Schizophrenia (SCZ) imposes a significant burden on patients and their families because of its high prevalence rate and disabling nature. Given the lack of definitive conclusions regarding its pathogenesis, physicians heavily rely on patients' subjective symptom descriptions for diagnosis because reliable diagnostic biomarkers are currently unavailable. The role of the inflammatory response in the pathogenesis of SCZ has been supported by some studies. The findings of these studies showed abnormal changes in the levels of inflammatory factors, such as cytokines (CKs), in both peripheral blood and cerebrospinal fluid (CSF) among individuals affected by SCZ. The findings imply that inflammatory factors could potentially function as risk indicators for the onset of SCZ. Consequently, researchers have directed their attention towards investigating the potential utility of CKs as viable biomarkers for diagnosing SCZ. Extracellular vesicles (EVs) containing disease-specific components exhibit remarkable stability and abundance, making them promising candidates for biomarker discovery across various diseases. CKs encapsulated within EVs secreted by immune cells offer valuable insights into disease progression. This review presents a comprehensive analysis summarizing the relationship between CKs and SCZ and emphasizes the vital role of CKs encapsulated within EVs in the pathogenesis and development of SCZ.

Aural Manifestations of Antineutrophil Cytoplasmic Antibody (ANCA)-Associated Vasculitis-Diagnosis, Symptoms, Treatment.

Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a group of vasculitis sharing a common pathophysiology, which affects small and medium blood vessels. There are three categories of AAV: granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), and eosinophilic granulomatosis with polyangiitis (EGPA). As a systemic disease, AAV can affect basically every organ. The goal of this publication is to sum up and underline the problem of the aural manifestation of AAV; it details the definition of Otitis Media with Antineutrophil Cytoplasmic Antibody Associated Vasculitis (OMAAV) and allows for a better understanding of the specific tasks of medical professionals taking part in the diagnostic and therapeutic process. Among others, this publication is directed to otolaryngologists who may encounter patients with AAV and often are the first specialists who see patients with early symptoms of AAV. This publication presents brief characteristics of AAV, descriptions of aural manifestations and symptoms, differential diagnosis, and both pharmacological and surgical treatment options, based on current recommendations and information found in the literature and clinical databases.

Patient perceptions of the impact of inducible laryngeal obstruction on quality of life.

Inducible laryngeal obstruction (ILO) accounts for or contributes to dyspnea in a noteworthy proportion of treatment seeking populations including those misdiagnosed with asthma. Despite increasing awareness of the disorder, literature exploring patient experience is limited. The aim of this work is to report patient perspectives on ILO and the way in which it impacts quality of life. This qualitative study utilized methods detailed in the literature on grounded theory and phenomenological research to analyze interviews collected from participants diagnosed with ILO. Interviews were conducted, audio recorded, and transcribed. Transcriptions underwent content-analysis using Burnard's 14 step method [15], which included review of content codes across multiple raters until consensus regarding analyses was reached. Twenty-six participants were included in the study. Most participants were female (92%). Ages ranged from 18-72 with a mean age of 45 for female participants and 37 for male participants. Without specific prompting to do so, all participants offered descriptions of the specific symptoms they experienced and the triggers for their symptoms. In the content analysis process, "descriptions of symptoms and triggers" was thus labeled a theme that was present in all interviews. Seven additional themes were shared consistently and judged to encapsulate the interview material. These themes were: 2) diagnosis and treatment, 3) emotional impact of ILO, 4) perception of health and prognosis, 5) ameliorating factors, 6) influence of ILO on lifestyle, 7) the physical impact of ILO, and 8), social consequences of ILO. In addition, 54 subthemes were identified. Patients appear to place particular emphasis on the emotional and psychosocial consequences of ILO as well as factors that ameliorate the condition. As such, future efforts to treat ILO and to collect outcomes measures should account for these aspects of the patient experience.