Journal of the American Medical Informatics Association最新文献

{"title":"Envisioning the future of primary care: intervention strategies to support patient-centered communication feedback technology.","authors":"Raina Langevin, Deepthi Mohanraj, Libby Shah, Janice Sabin, Brian R Wood, Wanda Pratt, Nadir Weibel, Andrea L Hartzler","doi":"10.1093/jamia/ocaf143","DOIUrl":"10.1093/jamia/ocaf143","url":null,"abstract":"<p><strong>Objective: </strong>Clinician implicit bias can impede patient-centered communication, leading to health care inequities. While the field of implicit bias education is evolving with advances in technology, clinicians' perspectives remain underexplored. This study investigated clinicians' perceptions of educational strategies to complement communication feedback technology in the implementation of an implicit bias education intervention.</p><p><strong>Materials and methods: </strong>We recruited primary care practitioners in remote interviews to brainstorm future technologies for improving clinician awareness of implicit bias in patient-provider communication. Participants completed an online survey in which they rated the priority of educational strategies that could complement the technology. We performed inductive-deductive thematic analysis of the interview data with Implicit Bias Recognition and Management (IBRM) domains as a priori codes and used descriptive statistics to summarize the survey data.</p><p><strong>Results: </strong>Participants (n = 16) proposed how future technology could improve clinician awareness, such as recording visits to help clinicians be more self-aware of their communication; however, some providers expressed concerns regarding feedback fatigue and the potential impact of technology on reducing time spent with patients. Participants recommended incorporating feedback regularly into training, identifying organizational incentives, and debriefing with trusted colleagues and communication experts.</p><p><strong>Discussion: </strong>Participants brainstormed technologies and identified educational strategies, such as discussion with a facilitator, that could promote clinician receptivity to feedback and inform IBRM approaches for clinical ambient intelligence. Yet, challenges remain to incentivizing participation for practicing clinicians, and Continuing Medical Education may be one effective approach.</p><p><strong>Conclusion: </strong>The proposed technologies and prioritized educational strategies have the potential to promote health equity by helping clinicians develop skills to manage implicit bias. In the future, these findings could inform IBRM interventions that leverage clinical ambient intelligence.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144975885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating the landscape of personalized oncology: overcoming challenges and expanding horizons with computational modeling.","authors":"Melike Sirlanci, David Albers, Jennifer Kwak, Clayton Smith, Tellen D Bennett, Steven M Bair","doi":"10.1093/jamia/ocaf144","DOIUrl":"https://doi.org/10.1093/jamia/ocaf144","url":null,"abstract":"<p><strong>Objectives: </strong>We discuss challenges using computational modeling approaches for personalized prediction in clinical practice to predict treatment response for rare diseases treated by novel therapies using clinical oncology as an example context. Several challenges are discussed, including data scarcity, data sparsity, and difficulties in establishing interdisciplinary teams. Machine learning (ML), mechanistic modeling (MM), and hybrid modeling (HM) are discussed in the context of these challenges.</p><p><strong>Materials and methods: </strong>We present an HM approach, combining ML and MM techniques for improved personalized model estimation in the context of chimeric antigen receptor T-cell therapy for aggressive lymphoma.</p><p><strong>Results: </strong>The HM approach improved the root mean squared error by 61.27±23.21% compared to using MM alone (MM: 2.36*105∓1.68*105and HM: 9.57*104∓8.37*104, where the units are in cells), computed from 13 patients included in this study.</p><p><strong>Discussion: </strong>By exploiting the complementary strengths of ML and MM approaches, the developed HM method addresses common limitations such as data scarcity and sparsity in medical settings, especially common for rare diseases.</p><p><strong>Conclusion: </strong>The HM techniques are likely required to overcome data scarcity and sparsity issues in broad medical settings. Developing these techniques requires dedicated interdisciplinary teams.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145001877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining housing insecurity and transportation barriers in pediatric hospital readmissions: insights from structured and unstructured data.","authors":"Shivani Mehta, William Brown, Urmimala Sarkar, Nathan Tran, Yulin Hswen, Matthew S Pantell","doi":"10.1093/jamia/ocaf135","DOIUrl":"https://doi.org/10.1093/jamia/ocaf135","url":null,"abstract":"<p><strong>Background: </strong>Pediatric hospital readmissions increase healthcare costs and highlight gaps in care. Social determinants of health (SDOH), such as housing and transportation insecurity, significantly impact outcomes but are underexplored in pediatric populations.</p><p><strong>Objectives: </strong>This study evaluates the impact of housing and transportation-related SDOH on pediatric readmissions, comparing structured ICD-10-CM Z-codes alone to a combination of structured and unstructured data extracted via natural language processing (NLP).</p><p><strong>Materials and methods: </strong>We conducted a retrospective cohort study of pediatric patients (ages 2-17) discharged from UCSF Benioff Children's Hospital between January 2018 and January 2023. SDOH exposure was identified using structured Z-codes and NLP-extracted data. The primary outcome was hospital readmission within 365 days. Cox proportional hazards models assessed associations between SDOH and readmission risk.</p><p><strong>Results: </strong>Among 8928 patients, only 0.8% were identified as exposed using structured data, compared to 31.7% using combined data. Patients identified through combined data had a higher readmission risk (HR: 2.64, 95% CI: 2.34-2.98) compared to those identified with structured data alone (HR: 1.99, 95% CI: 1.27-3.13). ED utilization was also higher among exposed patients. In the structured-only analysis, exposed patients had a significantly higher hazard of ED readmission (HR: 2.26, 95% CI: 1.65-3.10), whereas the association was slightly attenuated in the combined analysis (HR: 1.49, 95% CI: 1.37-1.62).</p><p><strong>Conclusion: </strong>Leveraging unstructured data enhances SDOH identification and reveals stronger associations with hospital and ED readmissions. A hybrid approach enables improved risk stratification and targeted interventions to address pediatric health disparities.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SMART: a new patient similarity estimation framework for enhanced predictive modeling in acute kidney injury.","authors":"Deyi Li, Alan S L Yu, Dana Y Fuhrman, Mei Liu","doi":"10.1093/jamia/ocaf125","DOIUrl":"https://doi.org/10.1093/jamia/ocaf125","url":null,"abstract":"<p><strong>Objective: </strong>Accurately measuring patient similarity is essential for precision medicine, enabling personalized predictive modeling, disease subtyping, and individualized treatment by identifying patients with similar characteristics to an index patient. This study aims to develop an electronic health record-based patient similarity estimation framework to enhance personalized predictive modeling for Acute Kidney Injury (AKI), a complex and life-threatening condition where accurate prediction is critical for timely intervention.</p><p><strong>Materials and methods: </strong>We introduce Similarity Measurement for Acute Kidney Injury Risk Tracking (SMART), a new patient similarity estimation framework with 3 key enhancements: (1) overlap weighting to adjust similarity scores; (2) distance measure optimization; and (3) feature type weight optimization. These enhancements were evaluated using internal and external validation datasets from 2 tertiary academic hospitals to predict AKI risk across varying group sizes of similar patients.</p><p><strong>Results: </strong>The study analyzed data from 8637 patients in the reference patient pool and 8542 patients in each of the internal and external test sets. Each enhancement was independently evaluated while controlling for other variables to determine its impact on prediction performance. SMART consistently outperformed 3 baseline models on both the internal and external test sets (P<.05) and demonstrated improved performance in certain subpopulations with unique health profiles compared to a traditional machine learning approach.</p><p><strong>Discussion: </strong>SMART improves the identification of high-quality similar patient groups, enhancing the accuracy of personalized AKI prediction across various group sizes. By accurately identifying clinically relevant similar patients, clinicians can tailor treatments more effectively, advancing personalized care.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing end-stage renal disease outcome prediction: a multisourced data-driven approach.","authors":"Yubo Li, Rema Padman","doi":"10.1093/jamia/ocaf118","DOIUrl":"https://doi.org/10.1093/jamia/ocaf118","url":null,"abstract":"<p><strong>Objectives: </strong>To improve prediction of chronic kidney disease (CKD) progression to end-stage renal disease (ESRD) using machine learning (ML) and deep learning (DL) models applied to integrated clinical and claims data with varying observation windows, supported by explainable artificial intelligence (AI) to enhance interpretability and reduce bias.</p><p><strong>Materials and methods: </strong>We utilized data from 10 326 CKD patients, combining clinical and claims information from 2009 to 2018. After preprocessing, cohort identification, and feature engineering, we evaluated multiple statistical, ML and DL models using 5 distinct observation windows. Feature importance and SHapley Additive exPlanations (SHAP) analysis were employed to understand key predictors. Models were tested for robustness, clinical relevance, misclassification patterns, and bias.</p><p><strong>Results: </strong>Integrated data models outperformed single data source models, with long short-term memory achieving the highest area under the receiver operating characteristic curve (AUROC) (0.93) and F1 score (0.65). A 24-month observation window optimally balanced early detection and prediction accuracy. The 2021 estimated glomerular filtration rate (eGFR) equation improved prediction accuracy and reduced racial bias, particularly for African American patients.</p><p><strong>Discussion: </strong>Improved prediction accuracy, interpretability, and bias mitigation strategies have the potential to enhance CKD management, support targeted interventions, and reduce health-care disparities.</p><p><strong>Conclusion: </strong>This study presents a robust framework for predicting ESRD outcomes, improving clinical decision-making through integrated multisourced data and advanced analytics. Future research will expand data integration and extend this framework to other chronic diseases.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing and sustaining inclusive language in biomedical informatics communications: an AMIA Board of Directors endorsed paper on the Inclusive Language and Context Style Guidelines.","authors":"Oliver Bear Don't Walk, Shefali Haldar, Duo Helen Wei, Hu Huang, Rebecca L Rivera, Jungwei W Fan, Vipina K Keloth, Tiffany I Leung, Pooja Desai, Diane M Korngiebel, Lisa Grossman Liu, Adrienne Pichon, Vignesh Subbian, Anthony Tony Solomonides, Laura K Wiley, Omolola Ogunyemi, Gretchen P Jackson, Irene Dankwa-Mullan, Lisa G Dirks, Avery Rose Everhart, Andrea G Parker, Bradley Iott, Clair Kronk, Randi Foraker, Krista Martin, Tara Anand, Salvatore G Volpe, Nathan Yung, Rubina Rizvi, Robert Lucero, Tiffani J Bright","doi":"10.1093/jamia/ocaf096","DOIUrl":"10.1093/jamia/ocaf096","url":null,"abstract":"<p><strong>Objectives: </strong>In 2023, AMIA's Inclusive Language and Context Style Guidelines (the \"Guidelines\") were approved by the Board of Directors and made a publicly available resource. This work began in 2021 through AMIA's DEI Task Force and subsequent DEI Committee; many members provided input, feedback, and time to create the Guidelines. In this paper, the authors provide a transparent account of the origin, development, contents, and dissemination of the Guidelines and share plans for their future development and use.</p><p><strong>Materials and methods: </strong>Our approach to drafting, refining, and distributing the Guidelines included consulting existing language guides, AMIA member reviews, external expert reviews, webinars, and workshops. Through an iterative approach to drafting and refining the Guidelines, the authors consulted relevant language guidelines and many experts throughout and beyond the AMIA community.</p><p><strong>Results: </strong>The Inclusive Language Context Guidelines were formally approved by the AMIA Board of Directors on February 15, 2023. The Guidelines included four principles to be considered in scientific communications: Plurality, Precision, Transparency, and Destigmatization.</p><p><strong>Discussion: </strong>A moment of vulnerability where an AMIA member raised concerns about the use of harmful language during a presentation resulted in the creation of a principled approach to support inclusive language within biomedical and health informatics communications. We envision that the Guidelines will support health equity by challenging dominant public narratives around health, fostering stronger interdisciplinary collaboration and critical thinking about the impact of language, and creating a more welcoming environment for the broader AMIA community. This work could not have been completed without the support of many AMIA members and other researchers in biomedical and health informatics. The Guidelines are a living document that will continue to be updated with input and feedback from the AMIA community into the future.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1380-1387"},"PeriodicalIF":4.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linguistic markers for identifying post-traumatic stress disorder and associated symptoms: a systematic literature review.","authors":"Robin Quillivic, Yann Auxéméry, Frédérique Gayraud, Jacques Dayan, Salma Mesmoudi","doi":"10.1093/jamia/ocaf075","DOIUrl":"10.1093/jamia/ocaf075","url":null,"abstract":"<p><strong>Objectives: </strong>Diagnosing post-traumatic stress disorder (PTSD) remains a challenge due to symptom variability and comorbidities. Linguistic analysis offers an innovative approach to identify PTSD symptoms and severity. This systematic review aimed at identifying linguistic features associated with PTSD, assessing the quality and limitations of existing studies, summarizing the predictive performance of identified models, and describing the clinical utility of these models.</p><p><strong>Materials: </strong>A comprehensive search was conducted across multiple databases, resulting in the identification of 593 articles. After screening and eligibility assessment, 58 studies were included.</p><p><strong>Methods: </strong>Data extraction focused on study characteristics, methodology, and performance metrics. We assessed the risk of bias using the PROBAST and conducted both a narrative synthesis and a meta-analysis.</p><p><strong>Results: </strong>Linguistic features such as pronoun use, emotional valence, cognitive processing words, narrative length, discourse disorganization, temporal orientation, specific lexical fields (death, anxiety, sensory-perception details), and disfluencies were commonly investigated. The meta-analysis revealed a pooled area under the curve of 0.81, indicating the high performance of classification models. However, significant publication bias and heterogeneity were noted. Only 8 studies were rated with a low risk of bias, highlighting common issues such as inadequate control groups, unvalidated linguistic tools, unvalidated diagnosis tools, and low rigor in statistical analysis.</p><p><strong>Discussion and conclusions: </strong>Linguistic markers showed potential for enhancing PTSD diagnoses, but the contemporary research was limited by methodological inconsistencies and biases. Future research should focus on standardized tools, symptom-focused studies, and interdisciplinary collaboration to improve the robustness and clinical applicability of findings.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1350-1363"},"PeriodicalIF":4.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144136524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting intracranial pressure monitor placement in children with traumatic brain injury: a prospective cohort study to develop a clinical decision support tool.","authors":"Seth Russell, Peter E DeWitt, Laura Helmkamp, Kathryn Colborn, Charlotte Gray, Margaret Rebull, Yamila L Sierra, Rachel Greer, Lexi Petruccelli, Sara Shankman, Todd C Hankinson, Fuyong Xing, David J Albers, Tellen D Bennett","doi":"10.1093/jamia/ocaf120","DOIUrl":"10.1093/jamia/ocaf120","url":null,"abstract":"<p><strong>Objective: </strong>Clinicians currently make decisions about placing an intracranial pressure (ICP) monitor in children with traumatic brain injury (TBI) without the benefit of an accurate clinical decision support tool. The goal of this study was to develop and validate a model that predicts placement of an ICP monitor and updates as new information becomes available.</p><p><strong>Materials and methods: </strong>A prospective observational cohort study was conducted from September 2014 to January 2024. The setting included one US hospital designated as an American College of Surgeons Level 1 Pediatric Trauma Center. Participants were 389 children with acute TBI admitted to the ICU who had at least one Glasgow Coma Scale (GCS) score ≤ 8 or intubation with at least one GCS-Motor ≤ 5. We excluded children who received ICP monitors prior to arrival, those with GCS = 3 and bilateral fixed, dilated pupils, and those with a do not resuscitate order.</p><p><strong>Results: </strong>Of the 389 participants, 138 received ICP monitoring. Several machine learning models, including a recurrent neural network (RNN), were developed and validated using 4 combinations of input data. The best performing model, an RNN, achieved an F1 of 0.71 within 720 minutes of hospital arrival. The cumulative F1 of the RNN from minute 0 to 720 was 0.61. The best performing non-neural network model, standard logistic regression, achieved an F1 of 0.36 within 720 minutes of hospital arrival.</p><p><strong>Conclusions: </strong>These findings will contribute to design and implementation of a multidisciplinary clinical decision support tool for ICP monitor placement in children with TBI.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using transfer learning to improve prediction of suicide risk in acute care hospitals.","authors":"Shane J Sacco, Kun Chen, Fei Wang, Steven C Rogers, Robert H Aseltine","doi":"10.1093/jamia/ocaf126","DOIUrl":"https://doi.org/10.1093/jamia/ocaf126","url":null,"abstract":"<p><strong>Objective: </strong>Emerging efforts to identify patients at risk of suicide have focused on the development of predictive algorithms for use in healthcare settings. We address a major challenge in effective risk modeling in healthcare settings with insufficient data with which to create and apply risk models. This study aimed to improve risk prediction using transfer learning or data fusion by incorporating risk information from external data sources to augment the data available in particular clinical settings.</p><p><strong>Materials and methods: </strong>In this retrospective study, we developed predictive models in individual Connecticut hospitals using medical claims data. We compared conventional models containing demographics and historical medical diagnosis codes with fusion models containing conventional features and fused risk information that described similarities in historical diagnosis codes between patients from the hospital and patients receiving care for suicide attempts at other hospitals.</p><p><strong>Results: </strong>Our sample contained 27 hospitals and 636 758 18- to 64-year-old patients. Fusion improved prediction for 93% of hospitals, while slightly worsening prediction for 7%. Median areas under the ROC and precision-recall curves of conventional models were 77.6% and 3.4%, respectively. Fusion improved these metrics by a median of 3.3 and 0.3 points, respectively (Ps < .001). Median sensitivities and positive predictive values at 90% and 95% specificity were also improved (Ps < .001).</p><p><strong>Discussion: </strong>This study provided strong evidence that data fusion improved model performance across hospitals. Improvement was of greatest magnitude in facilities treating relatively few suicidal patients.</p><p><strong>Conclusion: </strong>Data fusion holds promise as a methodology to improve suicide risk prediction in healthcare settings with limited or incomplete data.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144715164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biomedical and health informatics Potpourri.","authors":"Suzanne Bakken","doi":"10.1093/jamia/ocaf097","DOIUrl":"10.1093/jamia/ocaf097","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":"32 7","pages":"1091-1092"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}