Emotion-centered versus fact-centered medical information to alleviate pain and anxiety in prostate biopsy: A randomized trial.

BACKGROUND: To test the efficacy of emotion-centered (EC) versus fact-centered (FC) written medical information for prostate biopsy to alleviate pain and anxiety in a randomized controlled trial. METHODS: In a single-center, single-blinded study participants were randomized to receive FC or EC (DRKS00022361; 2020). In the EC, the focus was on possible stress reactions and stress-reducing strategies. Participants were asked to complete questionnaires on the day of MRI acquisition (T0) directly before (T1) and after the procedure (T2). The primary outcome measure was the assessment of worst pain in the last 2 h measured by the adapted brief pain inventory. Secondary outcome measures included state anxiety measured by the state-trait anxiety inventory and the subjective evaluation of the impact of the written medical information at T2. For statistical analysis, mixed models were calculated. RESULTS: Of 137 eligible patients, 108 (79%) could be recruited and were randomized. There was a significant effect for time for the outcome variables pain and anxiety. Regarding the comparison for the primary outcome variable worst pain there was a significantly lower increase from T1 to T2 after FC compared to EC (p < 0.004). The course of anxiety displayed no overall group differences. The FC was evaluated as significantly more helpful regarding stress, pain, and anxiety with moderate effect sizes. CONCLUSIONS: FC was favorable with regard to worst experienced pain, assuming that the brief introduction of emotional issues such as stress and coping in written information might be counterproductive particularly in men not used to these subjects.

Short-term usage of proton pump inhibitors during admission was associated with increased risk of rehospitalization in critically ill patients with myocardial infarction: a cohort study.

PURPOSE: Previous studies showed that long-term use of proton pump inhibitors (PPIs) was associated with cardiovascular events. However, the impact of short-term PPI exposure on intensive care unit (ICU) patients with myocardial infarction (MI) remains largely unknown. This study aims to determine the precise correlation between short-term PPI usage during hospitalization and prognostic outcomes of ICU-admitted MI patients using Medical Information Mart for Intensive Care IV database (MIMIC-IV). METHODS: Propensity score matching (PSM) was applied to adjust confounding factors. The primary study outcome was rehospitalization with mortality and length of stay as secondary outcomes. Binary logistic, multivariable Cox, and linear regression analyses were employed to estimate the impact of short-term PPI exposure on ICU-admitted MI patients. RESULTS: A total of 7249 patients were included, involving 3628 PPI users and 3621 non-PPI users. After PSM, 2687 pairs of patients were matched. The results demonstrated a significant association between PPI exposure and increased risk of rehospitalization for MI in both univariate and multivariate [odds ratio (OR) = 1.157, 95% confidence interval (CI) 1.020-1.313] analyses through logistic regression after PSM. Furthermore, this risk was also observed in patients using PPIs > 7 days, despite decreased risk of all-cause mortality among these patients. It was also found that pantoprazole increased the risk of rehospitalization, whereas omeprazole did not. CONCLUSION: Short-term PPI usage during hospitalization was still associated with higher risk of rehospitalization for MI in ICU-admitted MI patients. Furthermore, omeprazole might be superior to pantoprazole regarding the risk of rehospitalization in ICU-admitted MI patients.

Development and validation of a novel combinatorial nomogram model to predict in-hospital deaths in heart failure patients.

BACKGROUND: The purpose of this study was to develop a Nomogram model to identify the risk of all-cause mortality during hospitalization in patients with heart failure (HF). METHODS: HF patients who had been registered in the Medical Information Mart for Intensive Care (MIMIC) III and IV databases were included. The primary outcome was the occurrence of all-cause mortality during hospitalization. Two Logistic Regression models (LR1 and LR2) were developed to predict in-hospital death for HF patients from the MIMIC-IV database. The MIMIC-III database were used for model validation. The area under the receiver operating characteristic curve (AUC) was used to compare the discrimination of each model. Calibration curve was used to assess the fit of each developed models. Decision curve analysis (DCA) was used to estimate the net benefit of the predictive model. RESULTS: A total of 16,908 HF patients were finally enrolled through screening, of whom 2,283 (13.5%) presented with in-hospital death. Totally, 48 variables were included and analyzed in the univariate and multifactorial regression analysis. The AUCs for the LR1 and LR2 models in the test cohort were 0.751 (95% CI: 0.735∼0.767) and 0.766 (95% CI: 0.751-0.781), respectively. Both LR models performed well in the calibration curve and DCA process. Nomogram and online risk assessment system were used as visualization of predictive models. CONCLUSION: A new risk prediction tool and an online risk assessment system were developed to predict mortality in HF patients, which performed well and might be used to guide clinical practice.

Association between triglyceride glucose and acute kidney injury in patients with acute myocardial infarction: a propensity score­matched analysis.

BACKGROUND: Acute kidney injury (AKI) in patients with acute myocardial infarction (AMI) often indicates a poor prognosis. OBJECTIVE: This study aimed to investigate the association between the TyG index and the risk of AKI in patients with AMI. METHODS: Data were taken from the Medical Information Mart for Intensive Care (MIMIC) database. A 1:3 propensity score (PS) was set to match patients in the AKI and non-AKI groups. Multivariate logistic regression analysis, restricted cubic spline (RCS) regression and subgroup analysis were performed to assess the association between TyG index and AKI. RESULTS: Totally, 1831 AMI patients were included, of which 302 (15.6%) had AKI. The TyG level was higher in AKI patients than in non-AKI patients (9.30 ± 0.71 mg/mL vs. 9.03 ± 0.73 mg/mL, P < 0.001). Compared to the lowest quartile of TyG levels, quartiles 3 or 4 had a higher risk of AKI, respectively (Odds Ratiomodel 4 = 2.139, 95% Confidence Interval: 1.382-3.310, for quartile 4 vs. quartile 1, Ptrend < 0.001). The risk of AKI increased by 34.4% when the TyG level increased by 1 S.D. (OR: 1.344, 95% CI: 1.150-1.570, P < 0.001). The TyG level was non-linearly associated with the risk of AKI in the population within a specified range. After 1:3 propensity score matching, the results were similar and the TyG level remained a risk factor for AKI in patients with AMI. CONCLUSION: High levels of TyG increase the risk of AKI in AMI patients. The TyG level is a predictor of AKI risk in AMI patients, and can be used for clinical management.

Dr. Google to Dr. ChatGPT: assessing the content and quality of artificial intelligence-generated medical information on appendicitis.

INTRODUCTION: Generative artificial intelligence (AI) chatbots have recently been posited as potential sources of online medical information for patients making medical decisions. Existing online patient-oriented medical information has repeatedly been shown to be of variable quality and difficult readability. Therefore, we sought to evaluate the content and quality of AI-generated medical information on acute appendicitis. METHODS: A modified DISCERN assessment tool, comprising 16 distinct criteria each scored on a 5-point Likert scale (score range 16-80), was used to assess AI-generated content. Readability was determined using the Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL) scores. Four popular chatbots, ChatGPT-3.5 and ChatGPT-4, Bard, and Claude-2, were prompted to generate medical information about appendicitis. Three investigators independently scored the generated texts blinded to the identity of the AI platforms. RESULTS: ChatGPT-3.5, ChatGPT-4, Bard, and Claude-2 had overall mean (SD) quality scores of 60.7 (1.2), 62.0 (1.0), 62.3 (1.2), and 51.3 (2.3), respectively, on a scale of 16-80. Inter-rater reliability was 0.81, 0.75, 0.81, and 0.72, respectively, indicating substantial agreement. Claude-2 demonstrated a significantly lower mean quality score compared to ChatGPT-4 (p = 0.001), ChatGPT-3.5 (p = 0.005), and Bard (p = 0.001). Bard was the only AI platform that listed verifiable sources, while Claude-2 provided fabricated sources. All chatbots except for Claude-2 advised readers to consult a physician if experiencing symptoms. Regarding readability, FKGL and FRE scores of ChatGPT-3.5, ChatGPT-4, Bard, and Claude-2 were 14.6 and 23.8, 11.9 and 33.9, 8.6 and 52.8, 11.0 and 36.6, respectively, indicating difficulty readability at a college reading skill level. CONCLUSION: AI-generated medical information on appendicitis scored favorably upon quality assessment, but most either fabricated sources or did not provide any altogether. Additionally, overall readability far exceeded recommended levels for the public. Generative AI platforms demonstrate measured potential for patient education and engagement about appendicitis.

Association between the serum zinc level and nutritional status represented by the geriatric nutritional Rrisk index.

BACKGROUND: Although it is widely known that patients with chronic kidney disease (CKD) can develop zinc deficiency, in our previous analysis, the estimated glomerular filtration rate (eGFR) was not independently associated with the serum zinc level. Thus, a post hoc analysis was conducted to investigate the involvement of nutritional status. METHODS: A total of 655 subjects not on dialysis (402 males; mean age, 57 ± 18 years) who underwent serum zinc level measurements at Jikei University Hospital between April 2018 and March 2019 were selected using the Standardized Structured Medical Information eXchange2 (SS-MIX2) system. In addition, anthropometric data and the Geriatric Nutritional Risk Index (GNRI) representing nutritional status were obtained, and the relationship between the serum zinc level and nutritional status was investigated by multiple regression analysis. RESULTS: The serum albumin level and the GNRI were lower in the zinc-deficiency group, and both were positively associated with the serum zinc level (rho = 0.44, P < 0.01 and rho = 0.44, P < 0.01, respectively). On multiple regression analysis, the GNRI (t = 3.09, P < 0.01) and serum albumin level (t = 4.75, P < 0.01) were independently associated with the serum zinc level. Although a higher eGFR was associated with a higher serum zinc level, this association disappeared on multivariate analysis. CONCLUSION: In this post hoc analysis, the GNRI, as well as the serum albumin level, were correlated with the serum zinc level, indicating that nutritional status is an important determinant of the zinc level. Further investigations are needed to clarify the effects of nutritional status and kidney function on zinc deficiency.

Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis Study.

BACKGROUND: Intensive care research has predominantly relied on conventional methods like randomized controlled trials. However, the increasing popularity of open-access, free databases in the past decade has opened new avenues for research, offering fresh insights. Leveraging machine learning (ML) techniques enables the analysis of trends in a vast number of studies. OBJECTIVE: This study aims to conduct a comprehensive bibliometric analysis using ML to compare trends and research topics in traditional intensive care unit (ICU) studies and those done with open-access databases (OADs). METHODS: We used ML for the analysis of publications in the Web of Science database in this study. Articles were categorized into "OAD" and "traditional intensive care" (TIC) studies. OAD studies were included in the Medical Information Mart for Intensive Care (MIMIC), eICU Collaborative Research Database (eICU-CRD), Amsterdam University Medical Centers Database (AmsterdamUMCdb), High Time Resolution ICU Dataset (HiRID), and Pediatric Intensive Care database. TIC studies included all other intensive care studies. Uniform manifold approximation and projection was used to visualize the corpus distribution. The BERTopic technique was used to generate 30 topic-unique identification numbers and to categorize topics into 22 topic families. RESULTS: A total of 227,893 records were extracted. After exclusions, 145,426 articles were identified as TIC and 1301 articles as OAD studies. TIC studies experienced exponential growth over the last 2 decades, culminating in a peak of 16,378 articles in 2021, while OAD studies demonstrated a consistent upsurge since 2018. Sepsis, ventilation-related research, and pediatric intensive care were the most frequently discussed topics. TIC studies exhibited broader coverage than OAD studies, suggesting a more extensive research scope. CONCLUSIONS: This study analyzed ICU research, providing valuable insights from a large number of publications. OAD studies complement TIC studies, focusing on predictive modeling, while TIC studies capture essential qualitative information. Integrating both approaches in a complementary manner is the future direction for ICU research. Additionally, natural language processing techniques offer a transformative alternative for literature review and bibliometric analysis.

Utility of Large Language Models for Health Care Professionals and Patients in Navigating Hematopoietic Stem Cell Transplantation: Comparison of the Performance of ChatGPT-3.5, ChatGPT-4, and Bard.

BACKGROUND: Artificial intelligence is increasingly being applied to many workflows. Large language models (LLMs) are publicly accessible platforms trained to understand, interact with, and produce human-readable text; their ability to deliver relevant and reliable information is also of particular interest for the health care providers and the patients. Hematopoietic stem cell transplantation (HSCT) is a complex medical field requiring extensive knowledge, background, and training to practice successfully and can be challenging for the nonspecialist audience to comprehend. OBJECTIVE: We aimed to test the applicability of 3 prominent LLMs, namely ChatGPT-3.5 (OpenAI), ChatGPT-4 (OpenAI), and Bard (Google AI), in guiding nonspecialist health care professionals and advising patients seeking information regarding HSCT. METHODS: We submitted 72 open-ended HSCT-related questions of variable difficulty to the LLMs and rated their responses based on consistency-defined as replicability of the response-response veracity, language comprehensibility, specificity to the topic, and the presence of hallucinations. We then rechallenged the 2 best performing chatbots by resubmitting the most difficult questions and prompting to respond as if communicating with either a health care professional or a patient and to provide verifiable sources of information. Responses were then rerated with the additional criterion of language appropriateness, defined as language adaptation for the intended audience. RESULTS: ChatGPT-4 outperformed both ChatGPT-3.5 and Bard in terms of response consistency (66/72, 92%; 54/72, 75%; and 63/69, 91%, respectively; P=.007), response veracity (58/66, 88%; 40/54, 74%; and 16/63, 25%, respectively; P<.001), and specificity to the topic (60/66, 91%; 43/54, 80%; and 27/63, 43%, respectively; P<.001). Both ChatGPT-4 and ChatGPT-3.5 outperformed Bard in terms of language comprehensibility (64/66, 97%; 53/54, 98%; and 52/63, 83%, respectively; P=.002). All displayed episodes of hallucinations. ChatGPT-3.5 and ChatGPT-4 were then rechallenged with a prompt to adapt their language to the audience and to provide source of information, and responses were rated. ChatGPT-3.5 showed better ability to adapt its language to nonmedical audience than ChatGPT-4 (17/21, 81% and 10/22, 46%, respectively; P=.03); however, both failed to consistently provide correct and up-to-date information resources, reporting either out-of-date materials, incorrect URLs, or unfocused references, making their output not verifiable by the reader. CONCLUSIONS: In conclusion, despite LLMs' potential capability in confronting challenging medical topics such as HSCT, the presence of mistakes and lack of clear references make them not yet appropriate for routine, unsupervised clinical use, or patient counseling. Implementation of LLMs' ability to access and to reference current and updated websites and research papers, as well as development of LLMs trained in specialized domain knowledge data sets, may offer potential solutions for their future clinical application.

Evaluating the Efficacy of ChatGPT as a Patient Education Tool in Prostate Cancer: Multimetric Assessment.

BACKGROUND: Artificial intelligence (AI) chatbots, such as ChatGPT, have made significant progress. These chatbots, particularly popular among health care professionals and patients, are transforming patient education and disease experience with personalized information. Accurate, timely patient education is crucial for informed decision-making, especially regarding prostate-specific antigen screening and treatment options. However, the accuracy and reliability of AI chatbots' medical information must be rigorously evaluated. Studies testing ChatGPT's knowledge of prostate cancer are emerging, but there is a need for ongoing evaluation to ensure the quality and safety of information provided to patients. OBJECTIVE: This study aims to evaluate the quality, accuracy, and readability of ChatGPT-4's responses to common prostate cancer questions posed by patients. METHODS: Overall, 8 questions were formulated with an inductive approach based on information topics in peer-reviewed literature and Google Trends data. Adapted versions of the Patient Education Materials Assessment Tool for AI (PEMAT-AI), Global Quality Score, and DISCERN-AI tools were used by 4 independent reviewers to assess the quality of the AI responses. The 8 AI outputs were judged by 7 expert urologists, using an assessment framework developed to assess accuracy, safety, appropriateness, actionability, and effectiveness. The AI responses' readability was assessed using established algorithms (Flesch Reading Ease score, Gunning Fog Index, Flesch-Kincaid Grade Level, The Coleman-Liau Index, and Simple Measure of Gobbledygook [SMOG] Index). A brief tool (Reference Assessment AI [REF-AI]) was developed to analyze the references provided by AI outputs, assessing for reference hallucination, relevance, and quality of references. RESULTS: The PEMAT-AI understandability score was very good (mean 79.44%, SD 10.44%), the DISCERN-AI rating was scored as "good" quality (mean 13.88, SD 0.93), and the Global Quality Score was high (mean 4.46/5, SD 0.50). Natural Language Assessment Tool for AI had pooled mean accuracy of 3.96 (SD 0.91), safety of 4.32 (SD 0.86), appropriateness of 4.45 (SD 0.81), actionability of 4.05 (SD 1.15), and effectiveness of 4.09 (SD 0.98). The readability algorithm consensus was "difficult to read" (Flesch Reading Ease score mean 45.97, SD 8.69; Gunning Fog Index mean 14.55, SD 4.79), averaging an 11th-grade reading level, equivalent to 15- to 17-year-olds (Flesch-Kincaid Grade Level mean 12.12, SD 4.34; The Coleman-Liau Index mean 12.75, SD 1.98; SMOG Index mean 11.06, SD 3.20). REF-AI identified 2 reference hallucinations, while the majority (28/30, 93%) of references appropriately supplemented the text. Most references (26/30, 86%) were from reputable government organizations, while a handful were direct citations from scientific literature. CONCLUSIONS: Our analysis found that ChatGPT-4 provides generally good responses to common prostate cancer queries, making it a potentially valuable tool for patient education in prostate cancer care. Objective quality assessment tools indicated that the natural language processing outputs were generally reliable and appropriate, but there is room for improvement.

Clinical significance of the lactate-to-albumin ratio on prognosis in critically ill patients with acute kidney injury.

OBJECTIVE: To explore the relationship between lactate-to-albumin ratio (LAR) at ICU admission and prognosis in critically ill patients with acute kidney injury (AKI). METHODS: A retrospective analysis was conducted. Patients were divided into low (<0.659) LAR and high LAR (≥0.659) groups. Least absolute shrinkage and selection operator regression analysis was conducted to select variables associated with the 30-day prognosis. Cox regression analyses were performed to assess the association between LAR and mortality. Kaplan-Meier curves were plotted to compare cumulative survival rates between high and low LAR groups. Subgroup analysis was employed to assess the stability of the results. ROC curve was used to determine the diagnostic efficacy of LAR on prognosis. RESULTS: A nonlinear relationship was observed between LAR and the risk of 30-day and 360-day all-cause mortality in AKI patients (p < 0.001). Cox regulation showed that high LAR (≥ 0.659) was an independent risk factor for 30-day and 360-day all-cause mortality in patients with AKI (p < 0.001). The Kaplan-Meier survival curves demonstrated a noteworthy decrease in cumulative survival rates at both 30 and 360 days for the high LAR group in comparison to the low LAR group (p < 0.001). Subgroup analyses demonstrated the stability of the results. ROC curves showed that LAR had a diagnostic advantage when compared with lactate or albumin alone (p < 0.001). CONCLUSION: High LAR (≥0.659) at ICU admission was an independent risk factor for both short-term (30-day) and long-term (360-day) all-cause mortality in patients with AKI.

How Do Physicians Frame Medical Information in Talks With Their Patients? An Inductive Microanalysis.

During medical consultations, physicians need to share a substantial amount of information with their patients. How this information is framed can be crucial for patient understanding and outcomes, but little is known about the details of how physicians frame information in practice. Using an inductive microanalysis approach in the study of videotaped medical interactions, we aimed to identify the information frames (i.e., higher-level ways of organizing and structuring information to reach a particular purpose) and the information-framing devices (i.e., any dialogic mechanism used to present information in a particular way that shapes how the patient might perceive and interpret it) physicians use spontaneously and intuitively while sharing information with their patients. We identified 66 different information-framing devices acting within nine information frames conveying: (1) Do we agree that we share this knowledge?, (2) I don't like where I (or where you are) am going with this, (3) This may be tricky to understand, (4) You may need to think, (5) This is important, (6) This is not important, (7) This comes from me as a doctor, (8) This comes from me as a person, and (9) This is directed to you as a unique person. The kaleidoscope of information-framing devices described in this study reveals the near impossibility for neutrality and objectivity in the information-sharing practice of medical care. It also represents an inductively derived starting point for further research into aspects of physicians' information-sharing praxis.

The effect of electronic medical records on medication errors, workload, and medical information availability among qualified nurses in Israel- a cross sectional study.

BACKGROUND: Errors in medication administration by qualified nursing staff in hospitals are a significant risk factor for patient safety. In recent decades, electronic medical records (EMR) systems have been implemented in hospitals, and it has been claimed that they contribute to reducing such errors. However, systematic research on the subject in Israel is scarce. This study examines the position of the qualified nursing staff regarding the impact of electronic medical records systems on factors related to patient safety, including errors in medication administration, workload, and availability of medical information. METHODS: This cross-sectional study examines three main variables: Medication errors, workload, and medical information availability, comparing two periods- before and after EMR implementation based on self-reports. A final sample of 591 Israeli nurses was recruited using online private social media groups to complete an online structured questionnaire. The questionnaires included items assessing workload (using the Expanding Nursing Stress Scale), medical information availability (the Carrington-Gephart Unintended Consequences of Electronic Health Record Questionnaire), and medical errors (the Medical Error Checklists). Items were assessed twice, once for the period before the introduction of electronic records and once after. In addition, participants answered open-ended questions that were qualitatively analyzed. RESULTS: Nurses perceive the EMR as reducing the extent of errors in drug administration (mean difference = -0.92 ± 0.90SD, p < 0.001), as well as the workload (mean difference = -0.83 ± 1.03SD, p < 0.001) by ∼ 30% on average, each. Concurrently, the systems are perceived to require a longer documentation time at the expense of patients' treatment time, and they may impair the availability of medical information by about 10% on average. CONCLUSION: The results point to nurses' perceived importance of EMR systems in reducing medication errors and relieving the workload. Despite the overall positive attitudes toward EMR systems, nurses also report that they reduce information availability compared to the previous pen-and-paper approach. A need arises to improve the systems in terms of planning and adaptation to the field and provide appropriate technical and educational support to nurses using them.

Construction and evaluation of short-term and long-term mortality risk prediction model for patients with sepsis based on MIMIC-IV database. / 基于MIMIC-IVæž„å»ºåŠè¯„ä¼°è„“æ¯’ç—‡æ‚£è€ è¿‘æœŸå’Œè¿œæœŸæ­»äº¡é£Žé™©é¢„æµ‹æ¨¡åž‹.

OBJECTIVES: Given the high incidence and mortality rate of sepsis, early identification of high-risk patients and timely intervention are crucial. However, existing mortality risk prediction models still have shortcomings in terms of operation, applicability, and evaluation on long-term prognosis. This study aims to investigate the risk factors for death in patients with sepsis, and to construct the prediction model of short-term and long-term mortality risk. METHODS: Patients meeting sepsis 3.0 diagnostic criteria were selected from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and randomly divided into a modeling group and a validation group at a ratio of 7꞉3. Baseline data of patients were analyzed. Univariate Cox regression analysis and full subset regression were used to determine the risk factors of death in patients with sepsis and to screen out the variables to construct the prediction model. The time-dependent area under the curve (AUC), calibration curve, and decision curve were used to evaluate the differentiation, calibration, and clinical practicability of the model. RESULTS: A total of 14 240 patients with sepsis were included in our study. The 28-day and 1-year mortality were 21.45% (3 054 cases) and 36.50% (5 198 cases), respectively. Advanced age, female, high sepsis-related organ failure assessment (SOFA) score, high simplified acute physiology score II (SAPS II), rapid heart rate, rapid respiratory rate, septic shock, congestive heart failure, chronic obstructive pulmonary disease, liver disease, kidney disease, diabetes, malignant tumor, high white blood cell count (WBC), long prothrombin time (PT), and high serum creatinine (SCr) levels were all risk factors for sepsis death (all P<0.05). Eight variables, including PT, respiratory rate, body temperature, malignant tumor, liver disease, septic shock, SAPS II, and age were used to construct the model. The AUCs for 28-day and 1-year survival were 0.717 (95% CI 0.710 to 0.724) and 0.716 (95% CI 0.707 to 0.725), respectively. The calibration curve and decision curve showed that the model had good calibration degree and clinical application value. CONCLUSIONS: The short-term and long-term mortality risk prediction models of patients with sepsis based on the MIMIC-IV database have good recognition ability and certain clinical reference significance for prognostic risk assessment and intervention treatment of patients.

[Development of Preventive Methods for Drug-induced Cardiotoxicity Using a Large-scale Medical Information Database].

Cancer therapies have evolved considerably thereby substantially improving the survival of patients with cancer. However, cardiotoxicity, such as myocarditis and heart failure, induced by anticancer drugs, including immune checkpoint inhibitor(ICI)s and doxorubicin, present serious challenges. Numerous observations have indicated increased risks of cardiotoxicity- and cancer-related mortality in patients with drug-induced cardiotoxicity. Therefore, the prevention and management of drug-induced cardiotoxicity should be prioritized to enable sustainable long-term treatment while preserving patients' quality of life. Recently, medical research has been primarily focused on elucidation of therapeutic benefits and adverse events using medical big data, including worldwide databases of adverse events. The aim of the present study was to establish prevention strategies for drug-induced cardiotoxicity and advance data analytics. A data-driven approach was adopted to comprehensively analyze patient data and drug-induced cardiotoxicity. These data analytics revealed numerous risk factors, leading to the development of drugs that mitigate these factors. Furthermore, many unknown adverse events with molecularly targeted drugs were brought to light. Consequently, the importance of managing adverse events, guided by insights from data science, is predicted to increase. In this symposium review, we introduce our research exemplifying pharmaceutical studies utilizing medical big data. In particular, we discuss in detail the risk factors associated with myocarditis induced by immune checkpoint inhibitors along with prophylactic agents to mitigate doxorubicin-induced cardiotoxicity.

Dental Practice Websites in Germany-How Do They Inform about Fluoridation?

Fluoridation (Fl) is effective in preventing caries; however, it is unclear to what extent its use is counteracted by misinformation on the internet. This study aimed to evaluate the information provided on professional websites of German dental practices regarding fluoridation. A systematic search was performed by two independent examiners, utilizing three search engines, from 10 September 2021 to 11 December 2021. Modified, validated questionnaires (LIDA, DISCERN) were used to evaluate technical and functional aspects, generic quality, and risk of bias. Demographic information and statements about Fl were also collected. The intra- and inter-rater reliability assessments were excellent. Of the 81 websites analyzed, 64 (79%) mentioned Fl, and 31 (38%) indicated it as a primary focus. Most websites met at least 50% of the LIDA (90%) and DISCERN criteria (99%), indicating that the general quality was good. Thirty (37%) of the websites explained the impact of Fl, and forty-five (56%) indicated an opinion (for/against) on Fl. The practice location and the clinical focus were not associated with the overall quality of websites. Only a minority of websites explained the effects of Fl. Taken together, this study highlights that there is a distinct lack of good-quality information on FL.

How internet healthcare information overflow affects farmers' healthcare consumption: insights from China.

Introduction: In the context of the deep coupling and synergistic development of digital villages and healthy villages, the development of China's rural society harbors a huge potential for medical and healthcare consumption. Methods: On the basis of theoretical research, a framework was constructed to analyze the influence mechanism of farmers' medical and healthcare consumption in the context of Internet medical information overflow, and empirically examines the research and analysis framework by using the 2020 China Household Tracking Survey data with the OLS model, mediation effect model, and instrumental variable method. Results: It is found that Internet medical information spillover has a "crowding-in effect" on farmers' healthcare consumption; Medical attendance behavior, economic capital utilize the intermediary effect between Internet medical information spillover and farmers' healthcare consumption. And there is age group heterogeneity in the effect of Internet medical information spillover on farmers' healthcare consumption, The ability of rural middle-aged and old-aged groups to recognize new things such as Internet medical information needs to be improved, so the overflow of Internet medical information will induce rural middle-aged and old-aged groups to generate a certain amount of medical and health care consumption. However, the impact on healthcare consumption is not sensitive to the youth cohort group. Discussion: The sinking of Internet medical resources should be accelerated in the future to promote the high-quality development of rural medical and health services, at the same time the "Internet + healthcare services" should be optimized to promote scientific and rational stratification of farmers' access to healthcare, and economic capital for farmers' access to health care should be improved in order to alleviate the burden of health care, etc.

Relevance of a Customized Version of ChatGPT Explaining Laboratory Test Results in Patient Education.

ChatGPT is an artificial intelligence (AI) chatbot application. In this study, we explore the creation and use of a customized version of ChatGPT designed specifically for patient education, called "Lab Explainer." Lab Explainer aims to simplify and clarify the results of complex laboratory tests for patients, using the sophisticated capabilities of AI in natural language processing; it analyses various laboratory test data and provides clear explanations and contextual information. The approach involved adapting OpenAI's ChatGPT model specifically to analyze laboratory test data. The results suggest that Lab Explainer has the potential to improve understanding by providing an interpretation of laboratory tests to the patient. In conclusion, the Lab Explainer can assist patient education by providing intelligible interpretations of laboratory tests.

Exploring the current and prospective role of artificial intelligence in disease diagnosis.

Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems, providing assistance in a variety of patient care and health systems. The aim of this review is to contribute valuable insights to the ongoing discourse on the transformative potential of AI in healthcare, providing a nuanced understanding of its current applications, future possibilities, and associated challenges. The authors conducted a literature search on the current role of AI in disease diagnosis and its possible future applications using PubMed, Google Scholar, and ResearchGate within 10 years. Our investigation revealed that AI, encompassing machine-learning and deep-learning techniques, has become integral to healthcare, facilitating immediate access to evidence-based guidelines, the latest medical literature, and tools for generating differential diagnoses. However, our research also acknowledges the limitations of current AI methodologies in disease diagnosis and explores uncertainties and obstacles associated with the complete integration of AI into clinical practice. This review has highlighted the critical significance of integrating AI into the medical healthcare framework and meticulously examined the evolutionary trajectory of healthcare-oriented AI from its inception, delving into the current state of development and projecting the extent of reliance on AI in the future. The authors have found that central to this study is the exploration of how the strategic integration of AI can accelerate the diagnostic process, heighten diagnostic accuracy, and enhance overall operational efficiency, concurrently relieving the burdens faced by healthcare practitioners.

Gluten-free diet on video platforms: Retrospective infodemiology study.

Background: Video platform is an important approach for individuals to access and adopt health information. Online information on gluten-free diet (GFD) videos remains underinvestigated. Methods: GFD videos were identified by hashtag-based searching strategy. Videos' basic information, engagement metrics, and content were recorded. Mann-Kendall test was performed to examine time trends of submitting videos and engagement metrics. Video quality was evaluated by the DISCERN instrument and the HONcode. Results: A total of 822 videos were included in the analysis, with the majority focusing on gluten-free food recipes. The number of videos related to GFD was showing an upward trend. Engagement metrics varied between platforms and video types, with non-recipe videos receiving higher user engagement. The average DISCERN score was 50.20 out of 80 and the average HONcode score was 1.93 out of 8. Videos submitted by health professionals demonstrated better quality compared to those submitted by patients or general users. Conclusion: There was a rise in the number of videos related to GFD on Chinese video platforms. The overall quality of these videos was poor, most of them were not rigorous enough. Highlighting using social media as a health information source has the potential risk of disseminating one-sided messages and misleading. Efforts should be made to enhance the transparency of advertisements and establish clear guidelines for information sharing on social media platforms.

[Exploration of Risk Factors of the Onset of Antibiotics-induced Acute Kidney Injury and Its Transfer to Chronic Kidney Disease Using the Medical Information Database].

Drug-induced acute kidney injury (AKI) is a serious adverse drug reaction, which results in a significant decline in renal function and is known to progress to chronic kidney disease (CKD). Therefore, appropriate drug therapy is important to avoid the risk of drug-induced AKI and CKD, which are serious concerns in clinical practice. In this study, using the medical information database of Hamamatsu University Hospital, we investigated the risk factors that accelerate the onset of drug-induced AKI or its progression to CKD in patients who received aminoglycoside antibiotics (AGs) or glycopeptide antibiotics (GPs), which are strongly associated with drug-induced AKI and CKD. We performed logistic regression analysis using patients' background, laboratory test results, and concomitant drug use, among other such factors as explanatory variables and drug-induced AKI or CKD onset as objective variables to explore the risk factors for drug-induced AKI and CKD. Our results showed that co-administration of amphotericin B, piperacillin-tazobactam and other AGs and GPs, increased serum creatinine (Scr) and chloride concentrations, serum lactate dehydrogenase activity, and decreased serum albumin concentration were risk factors for drug-induced AKI onset. Moreover, a reduced blood urea nitrogen : Scr ratio at drug-induced AKI onset served as a risk factor for CKD. These results suggest that careful monitoring of the aforementioned factors is important to ensure appropriate usage of these drugs in patients treated with AGs and GPs.