JAMIA Open最新文献

{"title":"Case Report: A health system's experience using clinical decision support to promote note sharing after the 21st Century Cures Act.","authors":"Mark Iscoe, Arjun K Venkatesh, Emily M Powers, Nitu Kashyap, Allen L Hsiao, Hun Millard, Rohit B Sangal","doi":"10.1093/jamiaopen/ooaf051","DOIUrl":"10.1093/jamiaopen/ooaf051","url":null,"abstract":"<p><strong>Objective: </strong>We used clinical decision support (CDS) to promote compliance with the 21st Century Cures Act's mandate that, with few exceptions, patients be granted timely access to their clinical notes.</p><p><strong>Materials and methods: </strong>We conducted an observational analysis of note sharing rates in a large regional health system from February 2, 2021 to October 3, 2023. Throughout the study period, notes were shared with patients by default with the option not to grant note access; starting week 10, clinicians not sharing notes were presented with \"hard-stop\" CDS requiring selection of an allowable exception reason. Trends were examined with forward step-segmented linear regression.</p><p><strong>Results: </strong>0.7% of all notes were unshared; rates of unshared notes were highest in pediatrics (4.9%) and psychiatry (2.2%). Rates dropped substantially following hard-stop CDS introduction (downward step of 0.96%; 95% CI -1.17 to -0.024). Despite high portal access (72.6%), few notes were viewed by patients/proxies (17.0%).</p><p><strong>Discussion: </strong>We found very low overall rates of unshared notes; the significant drop in the rates of unshared notes following the introduction of hard-stop CDS is consistent with prior research showing that hard-stop CDS can be an effective tool. The higher rates of unshared notes in pediatrics and psychiatry likely reflect considerations around sensitive information that are inherent to these fields.</p><p><strong>Conclusions: </strong>CDS effectively promoted note sharing, but patient engagement remained low.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf051"},"PeriodicalIF":2.5,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12161449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144286664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Meaningfully meeting the interoperability mandate: a review of the Assistant Secretary for Technology Policy Real World Testing practices.","authors":"Jessica L Handley, Alicia Farlese, Sophia Lager, Ajit A Dhavle, Shahzad Ahmad, Anna Mathias, Raj M Ratwani","doi":"10.1093/jamiaopen/ooaf044","DOIUrl":"10.1093/jamiaopen/ooaf044","url":null,"abstract":"<p><strong>Objectives: </strong>We analyzed interoperability-related Real World Testing results to identify whether developers are providing meaningful results with the appropriate context to enable stakeholders to understand the Certified Health IT conformance and interoperability when deployed in production environments.</p><p><strong>Materials and methods: </strong>This qualitative study analyzed components of the Assistant Secretary for Technology Policy's transitions of care criterion Real World Testing results of 5 inpatient and 5 ambulatory health IT developers with the largest market share.</p><p><strong>Results: </strong>Developers provided interoperability measures; however, none of the developers' presented results in a meaningful way with the appropriate context to understand product interoperability.</p><p><strong>Discussion: </strong>Our results suggest that developers with ASTP/Office of the National Coordinator (ONC) Certified Health IT modules are not providing interoperability transparency through Real World Testing as required by the ONC Health IT Certification Program and intended by the 21st Century Cures Act.</p><p><strong>Conclusion: </strong>Clearer developer guidance and actual metric requirements on Real World Testing may be required and the authorized certification bodies, who review developer results, may need to more closely inspect reports to look at the quality of reported results.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf044"},"PeriodicalIF":2.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12153719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A community-engaged approach to developing common data elements: a case study from the RADx-UP Long COVID common data elements Task Force.","authors":"Helena L Pike Welch, Gregory Guest, Halima Garba, Gabriel A Carrillo, Allyn M Damman, Warren A Kibbe","doi":"10.1093/jamiaopen/ooaf046","DOIUrl":"10.1093/jamiaopen/ooaf046","url":null,"abstract":"<p><strong>Objectives: </strong>In response to requests from several Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) community-engaged research projects to include Long COVID common data elements (CDEs) in the existing RADx-UP CDEs, the RADx-UP Coordination and Data Collection Center (CDCC) leadership formed the Long COVID CDEs Task Force.</p><p><strong>Materials and methods: </strong>The Task Force, composed mainly of community partners and RADx-UP project members, participated in various activities to evaluate the Long COVID CDEs fit for purpose from the Researching COVID to Enhance Recovery (RECOVER) program for RADx-UP use.</p><p><strong>Results and discussion: </strong>The Task Force's efforts led to a compilation of lessons learned and the creation of a novel set of 28 CDEs that are appropriate for community-engaged research in Long COVID.</p><p><strong>Conclusion: </strong>Utilization of standardized CDEs does not always work for the communities involved in the research, but creation of a community-involved task force can lead to a meaningful, rich set of CDEs.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf046"},"PeriodicalIF":2.5,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12136053/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144226951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Help us document what we already do: pilot study of clinical decision support tools targeting social risk-informed care.","authors":"Maura Pisciotta, Suzanne Morrissey, Arwen Bunce, Laura M Gottlieb, Jenna Donovan, Shelby L Watkins, Mary Middendorf, Christina R Sheppler, Anna C Edelmann, Rachel Gold","doi":"10.1093/jamiaopen/ooaf045","DOIUrl":"10.1093/jamiaopen/ooaf045","url":null,"abstract":"<p><strong>Objective: </strong>Little is known about how clinical decision support (CDS) tools can support care teams in changing clinical decisions to account for patients' social risks. We piloted a suite of electronic health record (EHR)-based CDS tools designed to facilitate social risk-informed care decisions to assess how the tools were used in practice and how they could be improved.</p><p><strong>Materials and methods: </strong>After developing CDS tools through a process involving clinic staff and patient engagement, the tools were implemented in three community health center clinics. Data from staff interviews, observations of meetings with clinic staff, and the EHR were used to understand tool use patterns, and to yield insights that were then used to inform tool revisions.</p><p><strong>Results: </strong>The overarching suggestion derived from the study data was that the tools should shift from making care recommendations to instead supporting documentation of social risk-related actions that clinical team members had already taken. Other revisions were guided by four additional insights: the CDS tools should: (1) facilitate documentation in standardized, short formats, (2) make documentation easy and consistent, (3) support work distribution across care team members, and (4) ensure documentation could serve multiple purposes.</p><p><strong>Discussion: </strong>The CDS tools were revised to improve usefulness and acceptability for primary care teams in community clinics that serve patients with social risks. Numerous challenges exist in designing tools that can accommodate diverse clinics and workflows.</p><p><strong>Conclusion: </strong>These findings provide insights on how CDS tools can be optimized for social risk-informed care while minimizing care team burdens.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf045"},"PeriodicalIF":2.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12124400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimorbidity patterns and early signals of diabetes in online communities.","authors":"Ching Jin, Zhen Zhu","doi":"10.1093/jamiaopen/ooaf049","DOIUrl":"10.1093/jamiaopen/ooaf049","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to explore multimorbidity patterns associated with diabetes by analyzing user engagement in online diabetes support communities and their interactions with other disease-related communities. Additionally, it seeks to assess whether early signals of diabetes can be detected through online engagement data.</p><p><strong>Materials and methods: </strong>We collected Reddit data for 3 primary diabetes-related subreddits (\"diabetes,\" \"diabetes_t1,\" and \"diabetes_t2\") and 88 other disease-related subreddits from 2008 to 2024. A bipartite network was constructed linking users to subreddits, which was then transformed into a weighted multimorbidity network. Significant links were identified using a statistical threshold to ensure meaningful connections between subreddits. Additionally, we analyzed user engagement timelines to identify potential early signals of diabetes.</p><p><strong>Results: </strong>Diabetes is strongly linked to mental health conditions (such as depression, anxiety, and ADHD) and weight management discussions. Other notable associations include autoimmune diseases, chronic pain, gastrointestinal disorders, and reproductive health issues. Early signals of type 2 diabetes were detected in mental health, obesity, and pregnancy conditions, but no significant early indicators were found for type 1 diabetes.</p><p><strong>Discussion: </strong>This study is the first large-scale empirical analysis of multimorbidity patterns and early signals of diabetes in online communities. The findings reinforce the known multimorbidity of diabetes, particularly its ties to mental health and obesity. The presence of early signals suggests that social media data could help identify individuals at risk before diagnosis, offering opportunities for early intervention.</p><p><strong>Conclusion: </strong>Our findings demonstrate that social media data can reveal both multimorbidity patterns and early signals of diabetes, offering insights beyond traditional health records. As digital health data continue to grow, effectively leveraging these resources will become increasingly important for advancing diabetes prevention and management.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf049"},"PeriodicalIF":2.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12124401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does autotext usage decrease documentation time among resident physicians? A retrospective analysis of electronic health record usage data.","authors":"Noah Stanco, Shmuel Tiosano, Randeep Badwal, William Kelly, Michele R Lauria","doi":"10.1093/jamiaopen/ooaf042","DOIUrl":"10.1093/jamiaopen/ooaf042","url":null,"abstract":"<p><strong>Objective: </strong>Usage of autotext or \"dotphrases\" is ubiquitous among provider workflows in electronic health records (EHRs). Yet, little is known about the impact of these tools in inpatient settings and among resident physicians. We aimed to evaluate the association between autotext usage and documentation time among resident physicians in an academic medical center using the Cerner EHR.</p><p><strong>Materials and methods: </strong>The association between autotext executions and documentation time per patient seen for 705 resident physicians rotating at a large academic medical center from July 2021 to June 2023 was analyzed via linear regression after controlling for specialty, post-graduate year (PGY), provider gender and patient volume.</p><p><strong>Results: </strong>There was no significant overall association between autotext executions per patient seen and documentation time per patient seen in specialties using Dynamic Documentation as their primary workflow (β=-0.1 min per autotext execution per patient seen, 95% CI -0.6 to 0.5 min, <i>P =</i>.79). However, there was increased documentation time among residents with no autotext usage compared to residents who used autotext, and this effect was mediated by use of personalized autotexts. Specialty, PGY, gender and patient volume were significant determinants of documentation time.</p><p><strong>Discussion: </strong>Efforts to decrease documentation time among resident physicians should encourage autotext adoption but should not be focused on promotion of autotext usage alone. Further research should address the questions of identifying other determinants of documentation time, autotext design standards, and how autotext usage affects measures of note quality.</p><p><strong>Conclusion: </strong>Autotext adoption decreases documentation time among resident physicians, but among those who adopt autotext, higher levels of usage show no benefit.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf042"},"PeriodicalIF":2.5,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12118348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144175180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating clinicians' attitudes toward a web-based tool to support culturally and medically tailored nutrition services at the point of care.","authors":"Minakshi Raj, Haeley Peters, Margarita Teran-Garcia, Naiman Khan, Fangyu Zhou, Lisa Gatzke, Ian Brooks","doi":"10.1093/jamiaopen/ooaf043","DOIUrl":"10.1093/jamiaopen/ooaf043","url":null,"abstract":"<p><strong>Objectives: </strong>Despite growing recognition of the critical role of nutrition in promoting population health, clinicians lack access to point-of-care resources to support culturally relevant nutrition services. This study aims to (1) evaluate Registered Dietitian Nutritionists' (RDN) likelihood of using a web-based tool to provide culturally- and medically tailored nutrition services, (2) identify needed or preferred features, and (3) examine concerns related to the development or implementation of a web-based tool.</p><p><strong>Materials and methods: </strong>We conducted a cross-sectional, online survey of RDNs providing nutrition services in healthcare settings across the U.S. involving closed- and open-ended questions.</p><p><strong>Results: </strong>Of 155 RDNs, over 70% indicated being very or extremely likely to use a point-of-care web-based tool. Respondents sought content such as culturally-relevant recipes and an accessible tool that would integrate into their workflow. Concerns were related to quality of information provided and technical considerations such as data privacy.</p><p><strong>Discussion: </strong>Development of a web-based tool to support culturally- and medically tailored nutrition services may fill an unmet need within the healthcare workforce. This tool could be used as a point-of-care resource to optimize patient care and cultural inclusivity and could also function as a sustainable educational resource. Engaging culturally diverse patients and clinicians in tool development is critical for ensuring accessibility and optimal scope and quality of content. Privacy and security of information is essential to developing a trustworthy and equitable tool.</p><p><strong>Conclusion: </strong>Our findings suggest the need for a point of care web-based tool to support culturally- and medically tailored nutrition services across healthcare settings.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf043"},"PeriodicalIF":2.5,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12118349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144175182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review.","authors":"Lili M Schöler, Lisa Graf, Antti Airola, Alexander Ritzi, Michael Simon, Laura-Maria Peltonen","doi":"10.1093/jamiaopen/ooaf037","DOIUrl":"10.1093/jamiaopen/ooaf037","url":null,"abstract":"<p><strong>Objective: </strong>Delirium is a severe condition, often underreported and linked to adverse outcomes such as increased mortality and prolonged hospitalization. Despite its significance, delirium prediction is often hindered by underreporting and inconsistent labeling, highlighting the need for models trained on reliably labeled data (ground truth). This review examines (i) practices for determining labels in delirium prediction models and (ii) how study designs affect label quality, aiming to identify key considerations for improving model reliability.</p><p><strong>Materials and methods: </strong>A search of Cochrane, PubMed, and IEEE identified 120 studies that met the inclusion criteria.</p><p><strong>Results: </strong>To establish the ground truth, 40.8% of studies used routine data, while 42.5% used primary data. The Confusion Assessment Method (CAM) was the most widely used assessment tool (60. 0%). Label and data leakage occurred in 35.0% of studies. High Risk of Bias (RoB) was a recurring issue, with 31.7% of studies lacking sufficient reporting and 36.7% showing inadequate outcome determination. Studies using primary data had lower RoB, whereas those with unclear label sources displayed higher RoB.</p><p><strong>Discussion: </strong>Our findings underscore the importance of careful planning in determining the ground truth frequently neglected in existing studies. To address these challenges, we provide a decision support flowchart to guide the development of more accurate and reliable prediction models.</p><p><strong>Conclusion: </strong>This review uncovers significant variability in labeling methods and discusses how this may affect delirium prediction model reliability. Highlighting the importance of addressing underreporting bias and providing guidance for developing more robust models.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf037"},"PeriodicalIF":2.5,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12105575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated multi-instance REDCap data synchronization for NIH clinical trial networks.","authors":"Meredith C B Adams, Cody Hudson, Wanchi Chen, Robert W Hurley, Umit Topaloglu","doi":"10.1093/jamiaopen/ooaf036","DOIUrl":"10.1093/jamiaopen/ooaf036","url":null,"abstract":"<p><strong>Objectives: </strong>The main goal is to develop an automated process for connecting Research Electronic Data Capture (REDCap) instances in a clinical trial network to allow for deidentified transfer of research surveys to cloud computing data commons for discovery.</p><p><strong>Materials and methods: </strong>To automate the process of consolidating data from remote clinical trial sites into 1 dataset at the coordinating/storage site, we developed a Hypertext Preprocessor script that operates in tandem with a server-side scheduling system (eg, Cron) to set up practical data extraction schedules for each remote site.</p><p><strong>Results: </strong>The REDCap Application Programming Interface (API) Connection provides a novel implementation for automated synchronization between multiple REDCap instances across a distributed clinical trial network, enabling secure and efficient data transfer between study sites and coordination centers. Additionally, the protocol checker allows for automated reporting on conforming to planned data library protocols.</p><p><strong>Discussion: </strong>Working from a shared and accepted core library of REDCap surveys was critical to the success of this implementation. This model also facilitates Institutional Review Board (IRB) approvals because the coordinating center can designate which surveys and data elements to be transferred. Hence, protected health information can be transformed or withheld depending on the permission given by the IRB at the coordinating center level. For the NIH HEAL clinical trial networks, this unified data collection works toward the goal of creating a deidentified dataset for transfer to a Gen3 data commons.</p><p><strong>Conclusion: </strong>We established several simple and research-relevant tools, REDCAP API Connection and REDCAP Protocol Check, to support the emerging needs of clinical trial networks with increased data harmonization complexity.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf036"},"PeriodicalIF":2.5,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12103109/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144143827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Noninterruptive tool to support provider malnutrition documentation and minimize documentation queries.","authors":"Kevin O'Malley, Patricia Dasch, Sarah C Bauer, Dhananjay Vaidya, Matthew Severson, Sam Sokolinsky, Patricia Kaehne, Peter M Hill, Daniel J Brotman, Benjamin Erwin Bodnar, Stephen Lichtenstein, Renee Demski, Stephen A Berry","doi":"10.1093/jamiaopen/ooaf034","DOIUrl":"10.1093/jamiaopen/ooaf034","url":null,"abstract":"<p><strong>Objectives: </strong>Determine if an electronic documentation tool can reduce documentation queries for malnutrition without impacting diagnostic coding.</p><p><strong>Materials and methods: </strong>Malnutrition documentation queries and diagnosis coding proportions were compared between 2 groups of 600 malnourished adults discharged from internal medicine services before and after this electronic malnutrition documentation tool was promoted.</p><p><strong>Results: </strong>Documentation queries for malnutrition were observed in 300 (50%) of the preintervention discharges and 112 (19%) of the postintervention discharges (<i>P</i> < .001). A diagnosis code for malnutrition was observed in 99% of both groups. In a logistic regression accounting for clustering by provider, the odds ratio of a query postdeployment vs predeployment was 0.21 (95% CI, 0.16-0.29). In 88 of 112 (79%) of the postintervention discharges queried for malnutrition, the tool was not used as recommended.</p><p><strong>Conclusions: </strong>We have demonstrated that introducing and promoting this electronic documentation tool can reduce querying for malnutrition while preserving diagnostic coding.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"8 3","pages":"ooaf034"},"PeriodicalIF":2.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144120967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}