Description Of Symptoms Research Articles

Scalable information extraction from free text electronic health records using large language models

BackgroundA vast amount of potentially useful information such as description of patient symptoms, family, and social history is recorded as free-text notes in electronic health records (EHRs) but is difficult to reliably extract at scale, limiting their utility in research. This study aims to assess whether an “out of the box” implementation of open-source large language models (LLMs) without any fine-tuning can accurately extract social determinants of health (SDoH) data from free-text clinical notes.MethodsWe conducted a cross-sectional study using EHR data from the Mass General Brigham (MGB) system, analyzing free-text notes for SDoH information. We selected a random sample of 200 patients and manually labeled nine SDoH aspects. Eight advanced open-source LLMs were evaluated against a baseline pattern-matching model. Two human reviewers provided the manual labels, achieving 93% inter-annotator agreement. LLM performance was assessed using accuracy metrics for overall, mentioned, and non-mentioned SDoH, and macro F1 scores.ResultsLLMs outperformed the baseline pattern-matching approach, particularly for explicitly mentioned SDoH, achieving up to 40% higher Accuracymentioned. openchat_3.5 was the best-performing model, surpassing the baseline in overall accuracy across all nine SDoH aspects. The refined pipeline with prompt engineering reduced hallucinations and improved accuracy.ConclusionsOpen-source LLMs are effective and scalable tools for extracting SDoH from unstructured EHRs, surpassing traditional pattern-matching methods. Further refinement and domain-specific training could enhance their utility in clinical research and predictive analytics, improving healthcare outcomes and addressing health disparities.

Neuropsychiatric manifestations in systemic lupus erythematosus and Sjogren's disease.

Autoimmune diseases often present in a systemic manner, affecting various organs and tissues. Involvement of the central and peripheral nervous system is not uncommon in these conditions and is associated with high morbidity and mortality. Therefore, early recognition of the neuropsychiatric manifestations associated with rheumatologic diseases is essential for the introduction of appropriate therapies with the objective of providing a better quality of life for individuals. To provide a literature review of the neuropsychiatric manifestations related to Systemic Lupus Erythematosus (SLE) and primary Sjögren's Disease (pSD), through the description of signs, symptoms, and immunological variables associated with these conditions. A literature review was conducted by searching for national and international articles available in the SciELO and PubMed databases related to the description of neurological and psychiatric manifestations in patients with the rheumatologic diseases of interest in this study. The main NP manifestations presented in SLE and pSD are discussed, focusing on clinical presentation and etiology. Treatment option are, however, mainly based on expert opinion, since a few randomized controlled trials have been done. There is a high prevalence of neuropsychiatric manifestations associated with SLE and pSD. The variety of physiopathology pathways may explain the variety of symptoms, however pathological findings are rare. Multicenter studies on attribution protocols and treatment are necessary to address the current gaps.

Symptoms at population Eliciting Doses ≤ED05 for 11 priority allergenic foods are mild to moderate.

Insight into symptoms at low doses of protein from priority allergenic foods may support decision making and acceptance of harmonized reference doses for Precautionary Allergen Labeling (PAL). Symptoms were extracted from double-blind placebo-controlled food challenges underlying the full range Eliciting Dose (ED) distributions (Houben et al., 2020). Frequency and severity were analyzed at and below doses to which a maximum 10% of the allergic population is predicted to respond with objective symptoms (ED10). Detailed symptom descriptions at every dose were recorded for 1102 food challenges with 11 allergenic foods. At doses ≤ED10, generally, 1 to 2 symptoms, either objective or subjective, occurred per positive challenge (average 1.8±1.2, range 1-8). Symptoms were mostly (68%) subjective of nature. Most objective symptoms were in the skin (60-71%; e.g. flush, erythema), followed by eyes/nose and oral cavity (rhinorrhea, red eyes, lip swelling). Far less symptoms (approx. 5-8%) occurred in the gastrointestinal (vomiting) and respiratory tract (cough). Symptoms were graded mild to moderate, except for 2 cases of a severe symptom (wheeze, laryngeal edema), which occurred at a dose above the ED05 (approximately the ED08). Exposure ≤ED05 of priority allergenic foods resulted only in mild to moderate symptoms in a small proportion of the allergic population.

Exploring the experiences of cognitive symptoms in Long COVID: a mixed-methods study in the UK

ObjectiveTo explore the lived experiences and extent of cognitive symptoms in Long COVID (LC) in a UK-based sample.DesignThis study implemented a mixed-methods design. Eight focus groups were conducted to collect...

"Putting yourself in the shoes of others" - Relatability as a novel measure to explain the difference in stigma toward depression and schizophrenia.

Attitudes toward schizophrenia and depression have evolved differently over the last decades, exposing people with schizophrenia to growing stigma. Classic descriptions of schizophrenia symptoms as being particularly unrelatable might offer an explanation for this gap in attitudes that has not yet been tested. We examine to what extent relatability explains the difference in social distance toward people with depression or schizophrenia. We developed the 8-item "Relatability Scale", measuring to what extent people can relate to someone described as having either depression or schizophrenia, and used it in an online quota sample of 550 respondents in Germany. Beyond, we elicited the desire for social distance, continuum beliefs, emotional reactions, perceived dangerousness, general empathy, and previous contact. The Relatability Scale showed good psychometric properties and construct validity. Differences in relatability alone explained 63.6% of this difference in social distance between depression and schizophrenia. Adding continuum beliefs increased this amount to 83.0%. All other variables combined explained 53.2% of the difference in social distance between disorders. Differences in both relatability and continuum beliefs seem key to understanding different reactions to someone with depression or schizophrenia. Anti-stigma interventions could be optimized in order to increase relatability and continuum beliefs particularly regarding people with severe, psychotic mental disorders.

DrHouse: An LLM-empowered Diagnostic Reasoning System through Harnessing Outcomes from Sensor Data and Expert Knowledge

Large language models (LLMs) have the potential to transform digital healthcare, as evidenced by recent advances in LLM-based virtual doctors. However, current approaches rely on patient's subjective descriptions of symptoms, causing increased misdiagnosis. Recognizing the value of daily data from smart devices, we introduce a novel LLM-based multi-turn consultation virtual doctor system, DrHouse, which incorporates three significant contributions: 1) It utilizes sensor data from smart devices in the diagnosis process, enhancing accuracy and reliability. 2) DrHouse leverages continuously updating medical knowledge bases to ensure its model remains at diagnostic standard's forefront. 3) DrHouse introduces a novel diagnostic algorithm that concurrently evaluates potential diseases and their likelihood, facilitating more nuanced and informed medical assessments. Through multi-turn interactions, DrHouse determines the next steps, such as accessing daily data from smart devices or requesting in-lab tests, and progressively refines its diagnoses. Evaluations on three public datasets and our self-collected datasets show that DrHouse can achieve up to an 31.5% increase in diagnosis accuracy over the state-of-the-art baselines. The results of a 32-participant user study show that 75% medical experts and 91.7% test subjects are willing to use DrHouse.

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.

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.