BMC Medical Informatics and Decision Making最新文献

{"title":"The psychometric assessment of the provider version of mHealth App Usability Questionnaire (MAUQ) in persian language.","authors":"Sadrieh Hajesmaeel-Gohari, Abbas Sheikhtaheri, Fatemeh Dinari, Jamileh Farokhzadian, Kambiz Bahaadinbeigy, Khadijeh Moulaei","doi":"10.1186/s12911-024-02792-w","DOIUrl":"10.1186/s12911-024-02792-w","url":null,"abstract":"<p><strong>Introduction: </strong>mHealth apps are widely utilized in healthcare. To guarantee their usefulness and usability, it is crucial to assess them using a reliable scale tailored specifically for mHealth apps and their users.</p><p><strong>Objective: </strong>The aim of this study is the psychometric assessment of the provider version of mHealth App Usability Questionnaire (MAUQ) in Persian language.</p><p><strong>Method: </strong>The Persian translations of standalone and interactive versions of the MAUQ for healthcare providers underwent validation. Face validity, content validity, and factor analysis were conducted to validate these two versions. Ten nurses evaluated face validity, while ten nursing and psychometric analysis experts assessed content validity. Factor analysis involved 98 nurses. The reliability of the questionnaires was measured using Cronbach's alpha.</p><p><strong>Results: </strong>The translated questionnaires were validated, confirming both face validity (impact score ≥ 2.40) and content validity (k*≥0.66). The Persian version of the MAUQ for standalone applications had 18 items across two dimensions: easy to use and usefulness (11 items) and user interface and satisfaction (7 items). The Persian version of the MAUQ for interactive applications had 21 items across three dimensions: easy to use (4 items), usefulness (5 items), and user interface and satisfaction (12 items). Both standalone and interactive versions demonstrated high internal consistency with a Cronbach's alpha of 0.96.</p><p><strong>Conclusions: </strong>The psychometric assessment of the provider version of MAUQ in Persian language has the reliability and validity required to assess mHealth applications usability.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"369"},"PeriodicalIF":3.3,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11613550/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142766476","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":"Exploring parental factors influencing low birth weight on the 2022 CDC natality dataset.","authors":"Sumaiya Sultana Dola, Camilo E Valderrama","doi":"10.1186/s12911-024-02783-x","DOIUrl":"https://doi.org/10.1186/s12911-024-02783-x","url":null,"abstract":"<p><strong>Background and aims: </strong>Low birth weight (LBW), known as the condition of a newborn weighing less than 2500 g, is a growing concern in the United States (US). Previous studies have identified several contributing factors, but many have analyzed these variables in isolation, limiting their ability to capture the combined influence of multiple factors. Moreover, past research has predominantly focused on maternal health, demographics, and socioeconomic conditions, often neglecting paternal factors such as age, educational level, and ethnicity. Additionally, most studies have utilized localized datasets, which may not reflect the diversity of the US population. To address these gaps, this study leverages machine learning to analyze the 2022 Centers for Disease Control and Prevention's National Natality Dataset, identifying the most significant factors contributing to LBW across the US.</p><p><strong>Methods: </strong>We combined anthropometric, socioeconomic, maternal, and paternal factors to train logistic regression, random forest, XGBoost, conditional inference tree, and attention mechanism models to predict LBW and normal birth weight (NBW) outcomes. These models were interpreted using odds ratio analysis, feature importance, partial dependence plots (PDP), and Shapley Additive Explanations (SHAP) to identify the factors most strongly associated with LBW.</p><p><strong>Results: </strong>Across all five models, the most consistently associated factors with birth weight were maternal height, pre-pregnancy weight, weight gain during pregnancy, and parental ethnicity. Other pregnancy-related factors, such as prenatal visits and avoiding smoking, also significantly influenced birth weight.</p><p><strong>Conclusion: </strong>The relevance of maternal anthropometric factors, pregnancy weight gain, and parental ethnicity can help explain the current differences in LBW and NBW rates among various ethnic groups in the US. Ethnicities with shorter average statures, such as Asians and Hispanics, are more likely to have newborns below the World Health Organization's 2500-gram threshold. Additionally, ethnic groups with historical challenges in accessing nutrition and perinatal care face a higher risk of delivering LBW infants.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"367"},"PeriodicalIF":3.3,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11608488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142766468","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":"Analyzing evaluation methods for large language models in the medical field: a scoping review.","authors":"Junbok Lee, Sungkyung Park, Jaeyong Shin, Belong Cho","doi":"10.1186/s12911-024-02709-7","DOIUrl":"10.1186/s12911-024-02709-7","url":null,"abstract":"<p><strong>Background: </strong>Owing to the rapid growth in the popularity of Large Language Models (LLMs), various performance evaluation studies have been conducted to confirm their applicability in the medical field. However, there is still no clear framework for evaluating LLMs.</p><p><strong>Objective: </strong>This study reviews studies on LLM evaluations in the medical field and analyzes the research methods used in these studies. It aims to provide a reference for future researchers designing LLM studies.</p><p><strong>Methods & materials: </strong>We conducted a scoping review of three databases (PubMed, Embase, and MEDLINE) to identify LLM-related articles published between January 1, 2023, and September 30, 2023. We analyzed the types of methods, number of questions (queries), evaluators, repeat measurements, additional analysis methods, use of prompt engineering, and metrics other than accuracy.</p><p><strong>Results: </strong>A total of 142 articles met the inclusion criteria. LLM evaluation was primarily categorized as either providing test examinations (n = 53, 37.3%) or being evaluated by a medical professional (n = 80, 56.3%), with some hybrid cases (n = 5, 3.5%) or a combination of the two (n = 4, 2.8%). Most studies had 100 or fewer questions (n = 18, 29.0%), 15 (24.2%) performed repeated measurements, 18 (29.0%) performed additional analyses, and 8 (12.9%) used prompt engineering. For medical assessment, most studies used 50 or fewer queries (n = 54, 64.3%), had two evaluators (n = 43, 48.3%), and 14 (14.7%) used prompt engineering.</p><p><strong>Conclusions: </strong>More research is required regarding the application of LLMs in healthcare. Although previous studies have evaluated performance, future studies will likely focus on improving performance. A well-structured methodology is required for these studies to be conducted systematically.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"366"},"PeriodicalIF":3.3,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11606129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142754838","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":"Which criteria are important in usability evaluation of mHealth applications: an umbrella review.","authors":"Zahra Galavi, Mahdieh Montazeri, Reza Khajouei","doi":"10.1186/s12911-024-02738-2","DOIUrl":"10.1186/s12911-024-02738-2","url":null,"abstract":"<p><strong>Introduction: </strong>Usability plays a critical role in the design of mHealth applications. A well-designed app enhances user experience and contributes to better healthcare outcomes. However, it remains unclear which usability criteria are often neglected, leading to issues in the actual use of these applications. This study aimed to identify and categorize the usability issues of mHealth applications, mapping them to Nielsen's usability principles to determine the most critical criteria.</p><p><strong>Methods: </strong>The PRISMA guidelines were followed to report the results. Different databases (PubMed, Scopus, WoS) were searched for systematic reviews and/or meta-analyses about usability evaluation in mHealth applications. Two reviewers independently applied predefined selection criteria, extracted data, and assessed methodological quality using the AMSTAR tool.</p><p><strong>Results: </strong>Eight studies met the inclusion criteria. The most common method used in studies to evaluate the usability of mHealth applications was the questionnaire. Researchers identified 79 usability issues from the studies. Eleven of the issues were related to the Aesthetic and minimalist design category. The category of Flexibility and efficiency of use was next (n = 10).</p><p><strong>Conclusion: </strong>This study identified the usability issues that individuals face when using mHealth applications. By mapping these issues to evaluation criteria, developers can systematically address and prevent them. Attention to these issues will lead to better design and more effective use of mHealth applications.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"365"},"PeriodicalIF":3.3,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11606170/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142754748","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":"Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.","authors":"Ali Raza, Fatma Eid, Elisabeth Caro Montero, Irene Delgado Noya, Imran Ashraf","doi":"10.1186/s12911-024-02780-0","DOIUrl":"10.1186/s12911-024-02780-0","url":null,"abstract":"<p><p>Thyroid illness encompasses a range of disorders affecting the thyroid gland, leading to either hyperthyroidism or hypothyroidism, which can significantly impact metabolism and overall health. Hypothyroidism can cause a slowdown in bodily processes, leading to symptoms such as fatigue, weight gain, depression, and cold sensitivity. Hyperthyroidism can lead to increased metabolism, causing symptoms like rapid weight loss, anxiety, irritability, and heart palpitations. Prompt diagnosis and appropriate treatment are crucial in managing thyroid disorders and improving patients' quality of life. Thyroid illness affects millions worldwide and can significantly impact their quality of life if left untreated. This research aims to propose an effective artificial intelligence-based approach for the early diagnosis of thyroid illness. An open-access thyroid disease dataset based on 3,772 male and female patient observations is used for this research experiment. This study uses the nominal continuous synthetic minority oversampling technique (SMOTE-NC) for data balancing and a fine-tuned light gradient booster machine (LGBM) technique to diagnose thyroid illness and handle class imbalance problems. The proposed SNL (SMOTE-NC-LGBM) approach outperformed the state-of-the-art approach with high-accuracy performance scores of 0.96. We have also applied advanced machine learning and deep learning methods for comparison to evaluate performance. Hyperparameter optimizations are also conducted to enhance thyroid diagnosis performance. In addition, we have applied the explainable Artificial Intelligence (XAI) mechanism based on Shapley Additive exPlanations (SHAP) to enhance the transparency and interpretability of the proposed method by analyzing the decision-making processes. The proposed research revolutionizes the diagnosis of thyroid disorders efficiently and helps specialties overcome thyroid disorders early.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"364"},"PeriodicalIF":3.3,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11606065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142754843","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":"How to establish and sustain a disease registry: insights from a qualitative study of six disease registries in the UK.","authors":"Edmund Stubbs, Josephine Exley, Raphael Wittenberg, Nicholas Mays","doi":"10.1186/s12911-024-02775-x","DOIUrl":"10.1186/s12911-024-02775-x","url":null,"abstract":"<p><strong>Background: </strong>The advent of new chronic conditions such as long COVID-19 raises the question of whether and, if so, how best to establish new disease registries for such conditions. Prompted by the potential need for a long COVID-19 registry, we examined experiences of existing UK disease registries to understand barriers and enablers to establishing and sustaining a register, and how these have changed over time.</p><p><strong>Methods: </strong>We undertook semi-structured interviews between November 2022 and April 2023 with individuals representing six disease registries that collect individual-level longitudinal data on people diagnosed with a chronic condition.</p><p><strong>Results: </strong>Registries examined were developed by a few individuals, usually clinicians, to gain a greater understanding of the disease. Patient voices were largely absent from initial agenda setting processes, but, over time, all registries sought to increase patient involvement. Securing long-term funding was cited as the biggest challenge; due to limited funds, one of the registries examined no longer actively recruits patients. Charities devoted to the diseases in question were key funders, though most registries also sought commercial opportunities. Inclusion on the NIHR Clinical Research Network Portfolio was also considered a vital resource to support recruitment and follow-up of participants. All registries have sought to minimise the primary data collected to reduce the burden on clinicians and patients, increasingly relying on linkage to other data sources. Several registries have developed consent procedures that enable participants to be contacted for additional data collection. In some cases, the initial patient consent and data sharing permissions obtained had limited the flexibility to adapt the registry to changing data needs. Finally, there was a need to foster buy-in from the community of patients and clinicians who provide and/or use the data.</p><p><strong>Conclusion: </strong>We identified six key considerations when establishing a sustainable disease registry: (1) include a diverse set of stakeholders; (2) involve patients at every stage; (3) collect a core data set for all participants; (4) ensure the data system is flexible and interoperable with the wider data landscape; (5) anticipate changing data needs over time; and (6) identify financial opportunities to sustain the registry's activities for the long term.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"361"},"PeriodicalIF":3.3,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11603652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142738495","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":"Evaluation of low-and middle-income countries according to cardiovascular disease risk factors by using pythagorean fuzzy AHP and TOPSIS methods.","authors":"Gizem Zevde Aydın, Barış Özkan","doi":"10.1186/s12911-024-02769-9","DOIUrl":"10.1186/s12911-024-02769-9","url":null,"abstract":"<p><strong>Background: </strong>Cardiovascular disease risk factors play a crucial role in determining individuals' future health status and significantly affect health. This paper aimed to address cardiovascular disease risk factors in low- and middle-income countries using multi-criteria decision-making methods.</p><p><strong>Methods: </strong>In line with this objective, 22 evaluation criteria were identified. Due to the unequal importance levels of the criteria, the interval-valued Pythagorean Fuzzy AHP (PF-AHP) method was employed for weighting. The TOPSIS method was utilized to rank the countries.</p><p><strong>Results: </strong>The application of interval-valued PF-AHP revealed that metabolic, behavioral, and economic factors are more important in contributing to disease risk. Among adults, tobacco use prevalence was identified as the most significant risk factor. According to the TOPSIS method, Lebanon, Jordan, Solomon Islands, Serbia, and Bulgaria ranked highest, while Timor Leste, Benin, Ghana, Niger, and Ethiopia ranked lowest.</p><p><strong>Conclusions: </strong>Identifying disease risk factors and preventing or reducing risks are crucial in combating cardiovascular diseases. Therefore, it is recommended that countries ranking higher take remedial actions to reduce disease risk.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"363"},"PeriodicalIF":3.3,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142750102","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":"Correction: Systematic review and meta-analysis of workload among medical records coders in China.","authors":"Yu Liu, Chao Wu, Meiling Cao, Chunyan Lei, Zhiqiang Zhou, Wenjing Ou","doi":"10.1186/s12911-024-02782-y","DOIUrl":"10.1186/s12911-024-02782-y","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"362"},"PeriodicalIF":3.3,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11603795/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142750100","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":"Machine learning predicts pulmonary Long Covid sequelae using clinical data.","authors":"Ermanno Cordelli, Paolo Soda, Sara Citter, Elia Schiavon, Christian Salvatore, Deborah Fazzini, Greta Clementi, Michaela Cellina, Andrea Cozzi, Chandra Bortolotto, Lorenzo Preda, Luisa Francini, Matteo Tortora, Isabella Castiglioni, Sergio Papa, Diego Sona, Marco Alì","doi":"10.1186/s12911-024-02745-3","DOIUrl":"10.1186/s12911-024-02745-3","url":null,"abstract":"<p><p>Long COVID is a multi-systemic disease characterized by the persistence or occurrence of many symptoms that in many cases affect the pulmonary system. These, in turn, may deteriorate the patient's quality of life making it easier to develop severe complications. Being able to predict this syndrome is therefore important as this enables early treatment. In this work, we investigated three machine learning approaches that use clinical data collected at the time of hospitalization to this goal. The first works with all the descriptors feeding a traditional shallow learner, the second exploits the benefits of an ensemble of classifiers, and the third is driven by the intrinsic multimodality of the data so that different models learn complementary information. The experiments on a new cohort of data from 152 patients show that it is possible to predict pulmonary Long Covid sequelae with an accuracy of up to <math><mrow><mn>94</mn> <mo>%</mo></mrow> </math> . As a further contribution, this work also publicly discloses the related data repository to foster research in this field.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"359"},"PeriodicalIF":3.3,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11600907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142738498","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":"Development and validation of a rheumatoid arthritis case definition: a machine learning approach using data from primary care electronic medical records.","authors":"Anh N Q Pham, Claire E H Barber, Neil Drummond, Lisa Jasper, Doug Klein, Cliff Lindeman, Jessica Widdifield, Tyler Williamson, C Allyson Jones","doi":"10.1186/s12911-024-02776-w","DOIUrl":"10.1186/s12911-024-02776-w","url":null,"abstract":"<p><strong>Background: </strong>Rheumatoid Arthritis (RA) is a chronic inflammatory disease that is primarily diagnosed and managed by rheumatologists; however, it is often primary care providers who first encounter RA-related symptoms. This study developed and validated a case definition for RA using national surveillance data in primary care settings.</p><p><strong>Methods: </strong>This cross-sectional validation study used structured electronic medical record (EMR) data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Based on the reference set generated by EMR reviews by five experts, three machine learning steps: 'bag-of-words' approach to feature generation, feature reduction using a feature importance measure coupled with recursive feature elimination and clustering, and classification using tree-based methods (Decision Tree, Random Forest, and Extreme Gradient Boosting). The three tree-based algorithms were compared to identify the procedure that generated the optimal evaluation metrics. Nested cross-validation was used to allow evaluation and comparison and tuning of models simultaneously.</p><p><strong>Results: </strong>Of 1.3 million patients from seven Canadian provinces, 5,600 people aged 19 + were randomly selected. The optimal algorithm for selecting RA cases was generated by the XGBoost classification method. Based on feature importance scores for features in the XGBoost output, a human-readable case definition was created, where RA cases are identified when there are at least 2 occurrences of text \"rheumatoid\" in any billing, encounter diagnosis, or health condition table of the patient chart. The final case definition had sensitivity of 81.6% (95% CI, 75.6-86.4), specificity of 98.0% (95% CI, 97.4-98.5), positive predicted value of 76.3% (95% CI, 70.1-81.5), and negative predicted value of 98.6% (95% CI, 98.0-98.6).</p><p><strong>Conclusion: </strong>A case definition for RA in using primary care EMR data was developed based off the XGBoost algorithm. With high validity metrics, this case definition is expected to be a reliable tool for future epidemiological research and surveillance investigating the management of RA in CPCSSN dataset.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"360"},"PeriodicalIF":3.3,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11600853/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142738493","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}