Journal of Medical Systems最新文献

{"title":"Assessing the Relationship between Hospital Process Digitalization and Hospital Quality – Evidence from Germany","authors":"Justus Vogel, Alexander Haering, David Kuklinski, Alexander Geissler","doi":"10.1007/s10916-024-02101-y","DOIUrl":"https://doi.org/10.1007/s10916-024-02101-y","url":null,"abstract":"<p>Hospital digitalization aims to increase efficiency, reduce costs, and/ or improve quality of care. To assess a digitalization-quality relationship, we investigate the association between process digitalization and process and outcome quality. We use data from the German DigitalRadar (DR) project from 2021 and combine these data with two process (preoperative waiting time for osteosynthesis and hip replacement surgery after femur fracture, n = 516 and 574) and two outcome quality indicators (mortality ratio of patients hospitalized for outpatient-acquired pneumonia, n = 1,074; ratio of new decubitus cases, n = 1,519). For each indicator, we run a univariate and a multivariate regression. We measure process digitalization holistically by specifying three models with different explanatory variables: (1) the total DR-score (0 (not digitalized) to 100 (fully digitalized)), (2) the sum of DR-score sub-dimensions’ scores logically associated with an indicator, and (3) sub-dimensions’ separate scores. For the process quality indicators, all but one of the associations are insignificant. A greater DR-score is weakly associated with a lower mortality ratio of pneumonia patients (p &lt; 0.10 in the multivariate regression). In contrast, higher process digitalization is significantly associated with a higher ratio of decubitus cases (p &lt; 0.01 for models (1) and (2), p &lt; 0.05 for two sub-dimensions in model (3)). Regarding decubitus, our finding might be due to better diagnosis, documentation, and reporting of decubitus cases due to digitalization rather than worse quality. Insignificant and inconclusive results might be due to the indicators’ inability to reflect quality variation and digitalization effects between hospitals. For future research, we recommend investigating within hospital effects with longitudinal data.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review","authors":"Satoshi Takahashi, Yusuke Sakaguchi, Nobuji Kouno, Ken Takasawa, Kenichi Ishizu, Yu Akagi, Rina Aoyama, Naoki Teraya, Amina Bolatkan, Norio Shinkai, Hidenori Machino, Kazuma Kobayashi, Ken Asada, Masaaki Komatsu, Syuzo Kaneko, Masashi Sugiyama, Ryuji Hamamoto","doi":"10.1007/s10916-024-02105-8","DOIUrl":"https://doi.org/10.1007/s10916-024-02105-8","url":null,"abstract":"<p>In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive comparison of vision transformers (ViTs) and convolutional neural networks (CNNs), the two leading techniques in the field of deep learning in medical imaging. We conducted a survey systematically. Particular attention was given to the robustness, computational efficiency, scalability, and accuracy of these models in handling complex medical datasets. The review incorporates findings from 36 studies and indicates a collective trend that transformer-based models, particularly ViTs, exhibit significant potential in diverse medical imaging tasks, showcasing superior performance when contrasted with conventional CNN models. Additionally, it is evident that pre-training is important for transformer applications. We expect this work to help researchers and practitioners select the most appropriate model for specific medical image analysis tasks, accounting for the current state of the art and future trends in the field.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Responses of GPT-4 V to the Japanese National Clinical Engineer Licensing Examination","authors":"Kai Ishida, Naoya Arisaka, Kiyotaka Fujii","doi":"10.1007/s10916-024-02103-w","DOIUrl":"https://doi.org/10.1007/s10916-024-02103-w","url":null,"abstract":"<p>Chat Generative Pretrained Transformer (ChatGPT; OpenAI) is a state-of-the-art large language model that can simulate human-like conversations based on user input. We evaluated the performance of GPT-4 V in the Japanese National Clinical Engineer Licensing Examination using 2,155 questions from 2012 to 2023. The average correct answer rate for all questions was 86.0%. In particular, clinical medicine, basic medicine, medical materials, biological properties, and mechanical engineering achieved a correct response rate of ≥ 90%. Conversely, medical device safety management, electrical and electronic engineering, and extracorporeal circulation obtained low correct answer rates ranging from 64.8% to 76.5%. The correct answer rates for questions that included figures/tables, required numerical calculation, figure/table ∩ calculation, and knowledge of Japanese Industrial Standards were 55.2%, 85.8%, 64.2% and 31.0%, respectively. The reason for the low correct answer rates is that ChatGPT lacked recognition of the images and knowledge of standards and laws. This study concludes that careful attention is required when using ChatGPT because several of its explanations lack the correct description.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Medical Care during a Nerve Agent Mass Casualty Incident Using Computer Simulation.","authors":"De Rouck Ruben, Mehdi Benhassine, Debacker Michel, Van Utterbeeck Filip, Dhondt Erwin, Hubloue Ives","doi":"10.1007/s10916-024-02094-8","DOIUrl":"10.1007/s10916-024-02094-8","url":null,"abstract":"<p><strong>Introduction: </strong>Chemical mass casualty incidents (MCIs) pose a substantial threat to public health and safety, with the capacity to overwhelm healthcare infrastructure and create societal disorder. Computer simulation systems are becoming an established mechanism to validate these plans due to their versatility, cost-effectiveness and lower susceptibility to ethical problems.</p><p><strong>Methods: </strong>We created a computer simulation model of an urban subway sarin attack analogous to the 1995 Tokyo sarin incident. We created and combined evacuation, dispersion and victim models with the SIMEDIS computer simulator. We analyzed the effect of several possible approaches such as evacuation policy ('Scoop and Run' vs. 'Stay and Play'), three strategies (on-site decontamination and stabilization, off-site decontamination and stabilization, and on-site stabilization with off-site decontamination), preliminary triage, victim distribution methods, transport supervision skill level, and the effect of search and rescue capacity.</p><p><strong>Results: </strong>Only evacuation policy, strategy and preliminary triage show significant effects on mortality. The total average mortality ranges from 14.7 deaths in the combination of off-site decontamination and Scoop and Run policy with pretriage, to 24 in the combination of onsite decontamination with the Stay and Play and no pretriage.</p><p><strong>Conclusion: </strong>Our findings suggest that in a simulated urban chemical MCI, a Stay and Play approach with on-site decontamination will lead to worse outcomes than a Scoop and Run approach with hospital-based decontamination. Quick transport of victims in combination with on-site antidote administration has the potential to save the most lives, due to faster hospital arrival for definitive care.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11377464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142132918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Evaluation Framework for Medical LLMs: Combining Fuzzy Logic and MCDM for Medical Relation and Clinical Concept Extraction.","authors":"A H Alamoodi, Omar Zughoul, Dianese David, Salem Garfan, Dragan Pamucar, O S Albahri, A S Albahri, Salman Yussof, Iman Mohamad Sharaf","doi":"10.1007/s10916-024-02090-y","DOIUrl":"https://doi.org/10.1007/s10916-024-02090-y","url":null,"abstract":"<p><p>Artificial intelligence (AI) has become a crucial element of modern technology, especially in the healthcare sector, which is apparent given the continuous development of large language models (LLMs), which are utilized in various domains, including medical beings. However, when it comes to using these LLMs for the medical domain, there's a need for an evaluation platform to determine their suitability and drive future development efforts. Towards that end, this study aims to address this concern by developing a comprehensive Multi-Criteria Decision Making (MCDM) approach that is specifically designed to evaluate medical LLMs. The success of AI, particularly LLMs, in the healthcare domain, depends on their efficacy, safety, and ethical compliance. Therefore, it is essential to have a robust evaluation framework for their integration into medical contexts. This study proposes using the Fuzzy-Weighted Zero-InConsistency (FWZIC) method extended to p, q-quasirung orthopair fuzzy set (p, q-QROFS) for weighing evaluation criteria. This extension enables the handling of uncertainties inherent in medical decision-making processes. The approach accommodates the imprecise and multifaceted nature of real-world medical data and criteria by incorporating fuzzy logic principles. The MultiAtributive Ideal-Real Comparative Analysis (MAIRCA) method is employed for the assessment of medical LLMs utilized in the case study of this research. The results of this research revealed that \"Medical Relation Extraction\" criteria with its sub-levels had more importance with (0.504) than \"Clinical Concept Extraction\" with (0.495). For the LLMs evaluated, out of 6 alternatives, ( <math><mrow><mi>A</mi> <mn>4</mn></mrow> </math> ) \"GatorTron S 10B\" had the 1st rank as compared to ( <math><mrow><mi>A</mi> <mn>1</mn></mrow> </math> ) \"GatorTron 90B\" had the 6th rank. The implications of this study extend beyond academic discourse, directly impacting healthcare practices and patient outcomes. The proposed framework can help healthcare professionals make more informed decisions regarding the adoption and utilization of LLMs in medical settings.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142108163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mobile Apps for Wound Assessment and Monitoring: Limitations, Advancements and Opportunities.","authors":"Muhammad Ashad Kabir, Sabiha Samad, Fahmida Ahmed, Samsun Naher, Jill Featherston, Craig Laird, Sayed Ahmed","doi":"10.1007/s10916-024-02091-x","DOIUrl":"10.1007/s10916-024-02091-x","url":null,"abstract":"<p><p>With the proliferation of wound assessment apps across various app stores and the increasing integration of artificial intelligence (AI) in healthcare apps, there is a growing need for a comprehensive evaluation system. Current apps lack sufficient evidence-based reliability, prompting the necessity for a systematic assessment. The objectives of this study are to evaluate the wound assessment and monitoring apps, identify limitations, and outline opportunities for future app development. An electronic search across two major app stores (Google Play store, and Apple App Store) was conducted and the selected apps were rated by three independent raters. A total of 170 apps were discovered, and 10 were selected for review based on a set of inclusion and exclusion criteria. By modifying existing scales, an app rating scale for wound assessment apps is created and used to evaluate the selected ten apps. Our rating scale evaluates apps' functionality and software quality characteristics. Most apps in the app stores, according to our evaluation, do not meet the overall requirements for wound monitoring and assessment. All the apps that we reviewed are focused on practitioners and doctors. According to our evaluation, the app ImitoWound got the highest mean score of 4.24. But this app has 7 criteria among our 11 functionalities criteria. Finally, we have recommended future opportunities to leverage advanced techniques, particularly those involving artificial intelligence, to enhance the functionality and efficacy of wound assessment apps. This research serves as a valuable resource for future developers and researchers seeking to enhance the design of wound assessment-based applications, encompassing improvements in both software quality and functionality.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142046805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating EHR-Integrated Digital Technologies for Medication-Related Outcomes and Health Equity in Hospitalised Adults: A Scoping Review.","authors":"Sreyon Murthi, Nataly Martini, Nazanin Falconer, Shane Scahill","doi":"10.1007/s10916-024-02097-5","DOIUrl":"10.1007/s10916-024-02097-5","url":null,"abstract":"<p><p>The purpose of this scoping review is to identify and evaluate studies that examine the effectiveness and implementation strategies of Electronic Health Record (EHR)-integrated digital technologies aimed at improving medication-related outcomes and promoting health equity among hospitalised adults. Using the Consolidated Framework for Implementation Research (CFIR), the implementation methods and outcomes of the studies were evaluated, as was the assessment of methodological quality and risk of bias. Searches through Medline, Embase, Web of Science, and CINAHL Plus yielded 23 relevant studies from 1,232 abstracts, spanning 11 countries and from 2008 to 2022, with varied research designs. Integrated digital tools such as alert systems, clinical decision support systems, predictive analytics, risk assessment, and real-time screening and surveillance within EHRs demonstrated potential in reducing medication errors, adverse events, and inappropriate medication use, particularly in older patients. Challenges include alert fatigue, clinician acceptance, workflow integration, cost, data integrity, interoperability, and the potential for algorithmic bias, with a call for long-term and ongoing monitoring of patient safety and health equity outcomes. This review, guided by the CFIR framework, highlights the importance of designing health technology based on evidence and user-centred practices. Quality assessments identified eligibility and representativeness issues that affected the reliability and generalisability of the findings. This review also highlights a critical research gap on whether EHR-integrated digital tools can address or worsen health inequities among hospitalised patients. Recognising the growing role of Artificial Intelligence (AI) and Machine Learning (ML), this review calls for further research on its influence on medication management and health equity through integration of EHR and digital technology.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11341601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142036071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing ChatGPT and a Single Anesthesiologist's Responses to Common Patient Questions: An Exploratory Cross-Sectional Survey of a Panel of Anesthesiologists.","authors":"Frederick H Kuo, Jamie L Fierstein, Brant H Tudor, Geoffrey M Gray, Luis M Ahumada, Scott C Watkins, Mohamed A Rehman","doi":"10.1007/s10916-024-02100-z","DOIUrl":"https://doi.org/10.1007/s10916-024-02100-z","url":null,"abstract":"<p><p>Increased patient access to electronic medical records and resources has resulted in higher volumes of health-related questions posed to clinical staff, while physicians' rising clinical workloads have resulted in less time for comprehensive, thoughtful responses to patient questions. Artificial intelligence chatbots powered by large language models (LLMs) such as ChatGPT could help anesthesiologists efficiently respond to electronic patient inquiries, but their ability to do so is unclear. A cross-sectional exploratory survey-based study comprised of 100 anesthesia-related patient question/response sets based on two fictitious simple clinical scenarios was performed. Each question was answered by an independent board-certified anesthesiologist and ChatGPT (GPT-3.5 model, August 3, 2023 version). The responses were randomized and evaluated via survey by three blinded board-certified anesthesiologists for various quality and empathy measures. On a 5-point Likert scale, ChatGPT received similar overall quality ratings (4.2 vs. 4.1, p = .81) and significantly higher overall empathy ratings (3.7 vs. 3.4, p < .01) compared to the anesthesiologist. ChatGPT underperformed the anesthesiologist regarding rate of responses in agreement with scientific consensus (96.6% vs. 99.3%, p = .02) and possibility of harm (4.7% vs. 1.7%, p = .04), but performed similarly in other measures (percentage of responses with inappropriate/incorrect information (5.7% vs. 2.7%, p = .07) and missing information (10.0% vs. 7.0%, p = .19)). In conclusion, LLMs show great potential in healthcare, but additional improvement is needed to decrease the risk of patient harm and reduce the need for close physician oversight. Further research with more complex clinical scenarios, clinicians, and live patients is necessary to validate their role in healthcare.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142017797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Case-Study of Metoclopramide Prescription Error : A Grim Reminder.","authors":"Florent Wallet, Charlotte Doudet, Alexandre Theissen, Arnaud Friggeri, Charles-Hervé Vacheron","doi":"10.1007/s10916-024-02099-3","DOIUrl":"10.1007/s10916-024-02099-3","url":null,"abstract":"<p><p>The integration of Computerized Provider Order Entry (CPOE) systems in hospitals has been instrumental in reducing medication errors and enhancing patient safety. This study examines the implications of a software oversight in a CPOE system : Metoclopramide had a concentrated formulation (100 mg) delisted (and then not manufactured) in 2014 due to safety concerns. Despite this, the CPOE system continued to accept prescriptions for this formulation because it was not removed from the medication library by the pharmacist. The objective of our study was to describe this specific prescription error related to an outdated the medication library of the CPOE. We analyzed all metoclopramide prescriptions from 2014, to 2023. Our findings showed that errors involving 100 mg or more dosages were relatively rare, at 2.98 per 1000 prescriptions (34 errors in 11,372 prescriptions). Notably, 47.1% of these errors occurred during on-call shifts, and 68% of these errors led to actual administration. These errors correlated with periods of higher nurse workload. The findings advocate for the integration of dedicated pharmacists into ICU teams to minimize medication errors and enhance patient outcomes, and a proactive medication management in healthcare.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11341648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142017796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mixed Reality in the Operating Room: A Systematic Review.","authors":"Renato Magalhães, Ana Oliveira, David Terroso, Adélio Vilaça, Rita Veloso, António Marques, Javier Pereira, Luís Coelho","doi":"10.1007/s10916-024-02095-7","DOIUrl":"10.1007/s10916-024-02095-7","url":null,"abstract":"<p><p>Mixed Reality is a technology that has gained attention due to its unique capabilities for accessing and visualizing information. When integrated with voice control mechanisms, gestures and even iris movement, it becomes a valuable tool for medicine. These features are particularly appealing for the operating room and surgical learning, where access to information and freedom of hand operation are fundamental. This study examines the most significant research on mixed reality in the operating room over the past five years, to identify the trends, use cases, its applications and limitations. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to answer the research questions established using the PICO (Population, Intervention, Comparator and Outcome) framework. Although implementation of Mixed Reality applications in the operations room presents some challenges, when used appropriately, it can yield remarkable results. It can make learning easier, flatten the learning curve for several procedures, and facilitate various aspects of the surgical processes. The articles' conclusions highlight the potential benefits of these innovations in surgical practice while acknowledging the challenges that must be addressed. Technical complexity, equipment costs, and steep learning curves present significant obstacles to the widespread adoption of Mixed Reality and computer-assisted evaluation. The need for more flexible approaches and comprehensive studies is underscored by the specificity of procedures and limited samples sizes. The integration of imaging modalities and innovative functionalities holds promise for clinical applications. However, it is important to consider issues related to usability, bias, and statistical analyses. Mixed Reality offers significant benefits, but there are still open challenges such as ergonomic issues, limited field of view, and battery autonomy that must be addressed to ensure widespread acceptance.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11327191/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141982525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}