Prevention Science最新文献

{"title":"Disseminating Health Prevention Programs Using AI-Crafted, Culturally Tailored Ads for Somali American Adolescents.","authors":"Tori Simenec, Salma Ibrahim, Gail Ferguson","doi":"10.1007/s11121-026-01891-6","DOIUrl":"10.1007/s11121-026-01891-6","url":null,"abstract":"<p><p>Recent advancements in artificial intelligence (AI) and digital health messaging present opportunities to bridge service health gaps in underserved communities. Adolescents are highly engaged with technology; however, they lack the access to relevant health information and resources tailored to their specific needs. This may be especially true for Black migrant-background adolescents. Utilizing publicly available AI resources can support the dissemination of culturally responsive digital prevention programs. The present study developed four 1-min direct-to-consumer (DTC) video health advertisements that varied in cultural and developmental tailoring, using focus-group-generated digital health message preferences. The advertisements promoted a culturally adapted food-focused media literacy program, JUS Media? Global Classroom - Somali American. The four ads were experimentally tested among 230 Somali American adolescents (ages 11-19, M<sub>age</sub> = 14.9; 67.5% female) who through age-stratified randomization viewed the DTC video ads. Surveys assessed personal relevance, elaboration (i.e., critical thinking after receiving the message), message liking, and intentions to use the advertised online health intervention. Hierarchical regression analyses revealed that cultural adaptation significantly increased three of four primary indicators of program effectiveness-personal relevance, liking, and usage intentions-whereas developmental adaptation did not significantly predict outcome variables. Insights from the study offer novel strategies in applying human-centered AI to co-develop tailored digital advertisements promoting digital prevention programs. Cultural tailoring of health messages has been criticized for requiring a substantial degree of human and financial capital. Publicly available AI tools can efficiently design tailored develop tailored direct-to-consumer (DTC) ads to disseminate digital health programs and overcome logistical barriers. This is important given delivering health interventions to youth, perhaps especially Black migrant-background youth, is often challenging due to institutional gatekeepers and lack of culturally responsive interventions and recruitment/dissemination strategies (Hill, L., Ndugga, N., & Artiga, S. (2024, June 11). Key data on health and health care by race and ethnicity. KFF. https://www.kff.org/key-data-on-health-and-health-care-by-race-and-ethnicity/). To propel accessibility and scalability of services to support Black migrant youth in the U.S., who are at risk for unhealthy eating due to disproportionate targeting of unhealthy food messages, prevention and intervention programs can capitalize on the very same tactics used by junk food advertisers to strategically develop and disseminate health services. By leveraging Artificial Intelligence (AI) to develop DTC marketing of digital prevention programs, service providers can effectively reach audiences most likely to benefit, offering a more cost-","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1111-1124"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147481973","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":"Comparing AI-Assisted Coding and Traditional Qualitative Analysis: a Study Examining Differences in Methods and Results of AI-Assisted Coding and Traditional Content Coding Using Community Engagement Data Collected During the Development of a Municipal Food Plan.","authors":"Natalie Poulos, Hannah Price, Lauren Bell, Courtney Byrd-Williams, Sergio Torres-Peralta, Edwin Marty","doi":"10.1007/s11121-026-01904-4","DOIUrl":"10.1007/s11121-026-01904-4","url":null,"abstract":"<p><p>Qualitative data poses a challenge for prevention science and public health, as it is critical to explain the context of communities, health, and behavior, yet collecting and analyzing qualitative data using traditional methods is time-intensive and requires extensive training. As artificial intelligence (AI) models have improved, there is a growing interest in using AI to code qualitative data quickly and reliably. This study compares the similarities and differences in methods and results of artificial intelligence (AI)-assisted qualitative analysis to traditional qualitative content analysis using data collected during the development of a city and county-based food plan. In total, 2820 community comments were collected across 43 community events in 27 zip codes across the region between March 2023 and January 2024. AI-assisted analysis was completed using a combination of a transcription app (Post-It^Ⓡ), GPT4 Plus, and GPT for Sheets with oversight from a public health practitioner. Traditional qualitative content analysis was completed with two trained coders who completed codebook development, reliability analysis, and full content coding. Both methods used deductive codes to represent key aspects of the food system and generated inductive codes to represent areas not included by the deductive food system codes. Results found that AI-assisted methods and traditional content analysis produced similar deductive coding results, while inductive coding results were less comparable across methods. Given that qualitative data has become a central part of prevention science, we believe with careful considerations, AI-assisted methods with intentional oversight have the potential to strengthen our ability to process large amounts of qualitative data.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1163-1175"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147522388","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":"Leveraging Machine Learning to Understand the Link Between School Climate and Youth Substance Use: a Focus on Cannabis and Alcohol Use.","authors":"Ali Ünlü, Candra Skrzypek, Catherine P Bradshaw","doi":"10.1007/s11121-026-01887-2","DOIUrl":"10.1007/s11121-026-01887-2","url":null,"abstract":"<p><p>This paper focuses on school climate indicators, which have been previously linked with aspects of students' well-being and school-related success, to explore how they relate to alcohol and cannabis use. We used machine learning (ML) approaches and leveraged data from a diverse sample of 69,513 students (45.4% White, 23.9% Black, 8.9% Latine) across 111 middle and high schools, with 12% (n = 7783) reporting cannabis use and 18.8% (n = 12,220) reporting alcohol use in the past 30 days. We focused on 154 items related to school climate, student attitudes and behaviors, and demographics. We employed a two-stage feature selection method, initially reducing the 154 features to 31, and subsequently to 20, for both alcohol and cannabis use. Alcohol and cannabis use shared 15 common features and 5 distinct features, though some variation occurred across these two outcome variables. We identified both unique and shared factors that best classified current users vs. non-users. Specifically, gender, sense of pride in the school, weapon carrying, and bullying others were unique indicators that best classified alcohol use. In contrast, difficulties overcoming challenges, problems controlling temper, and becoming angry easily were more strongly associated with cannabis use. Shared indicators associated with both substances included gang membership, skipping school, violent behavior, school-parent and school-student engagement, and gambling. The inclusion of diverse classification factors underscored ML's ability to capture complex social and environmental factors that may be associated with substance use differently across student demographics. These features were tested in 12 classification models for both substances, achieving ROC-AUC scores up to 86% with fine-tuning of the best-performing models. The results highlight the utility of ML for examining complex, multidimensional indicators associated with substance use that complement traditional models.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1064-1076"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550096/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147318682","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":"Using Machine Learning Methodology to Identify Predictors of Non-Response to MTSS-B: a Focus on Discipline Problems and Juvenile Justice Involvement.","authors":"Kate Somerville, Ali Ünlü, Angela K Henneberger, Bess A Rose, Elise T Pas, Catherine P Bradshaw","doi":"10.1007/s11121-026-01932-0","DOIUrl":"10.1007/s11121-026-01932-0","url":null,"abstract":"<p><p>Multi-tiered systems of support for behavior (MTSS-B) is a widely-used tiered preventive intervention currently used in over 25,000 schools across the USA. This study leveraged data from 16,907 elementary students across 42 schools that participated in a prior randomized controlled trial (RCT) of MTSS-B, where schools were randomly assigned to implement Tier 1 + 2 (intervention) supports or Tier 1 only (comparison). The RCT data, collected between 2008 and 2012, were linked to administrative records of behavior in grades 6-12 collected between 2008 and 2024. We used machine learning methods to identify elementary school predictors of three long-term outcomes (i.e., in-school suspension (ISS), out-of-school suspension (OSS), and arrest) signifying Tier 1 non-response. For ISS, the strongest predictors were family involvement, being female, and internalizing problems, which serve as protective factors. For OSS, they were family involvement, internalizing problems, and academic performance, all of which were protective. For arrest, key predictors included family problems, where lower problems were protective against arrest, and prosocial behavior and family involvement, which were both protective. The top predictors were similar for students in the Tier 1 + 2 and Tier 1 only conditions. These findings can inform early screening for possible non-response to MTSS-B and highlight the need for family engagement practices.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1140-1152"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550068/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147989568","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":"Identifying and Prioritizing Core Components of Relationship Education Programs: a Case Study of an Artificial Intelligence (AI) Assisted Systematic Review.","authors":"Claire Kelley, Sarah Kelley, Matthew Rivas-Koehl, April Wilson, Kristina Rosinsky, Sydney Briggs, Mindy E Scott","doi":"10.1007/s11121-026-01885-4","DOIUrl":"10.1007/s11121-026-01885-4","url":null,"abstract":"<p><p>The field of prevention science seeks to identify and implement effective strategies to address social, emotional, and health challenges. A critical aspect of this endeavor is determining the core components of prevention programs that drive positive outcomes. This article presents a case study utilizing artificial intelligence (AI)-assisted systematic review methods to identify key components of healthy marriage and relationship education programs. Given the growing body of research in this domain, AI tools offer a promising means to enhance the efficiency and accuracy of literature reviews. This study employed AI to screen, code, and validate research articles, demonstrating its effectiveness in expediting systematic reviews while maintaining high accuracy in inclusion screening. This case study involved a systematic review of 22,028 resources (identified from PsycINFO, Academic Search Ultimate, and Google) and a final data set of 268 relevant studies. AI screening was integral in effectively conducting multiple rounds of screening. However, findings also highlight challenges in AI-assisted qualitative data abstraction, underscoring the continued need for human expertise in complex coding tasks. The study contributes to the ongoing discourse on integrating AI into prevention science methodologies and offers insights for optimizing AI applications in systematic reviews.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1191-1202"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146214665","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":"Addressing Health Disparities through Community Engagement in Artificial Intelligence-Driven Prevention Science.","authors":"Emily E Haroz, Novalene Goklish, Adrienne Dillard, Roy Adams, Sheana S Bull, Ricardo F Gonzalez-Fisher, Pamela Valenza, Spero M Manson, Roland J Thorpe","doi":"10.1007/s11121-025-01863-2","DOIUrl":"10.1007/s11121-025-01863-2","url":null,"abstract":"<p><p>Artificial intelligence and machine learning (AI/ML) in prevention science may improve or perpetuate health inequities. Community engagement is one proposed strategy thought to empirically mitigate bias in AI/ML tools. We outline how to incorporate community engagement at every stage of the model development and implementation. Borrowing from a framework for phases of prevention research, we describe the value and application of engaging communities to help shape more rigorous and relevant applications of AI/ML for prevention science. We provide concrete examples from real-world applications, including efforts in suicide prevention with Indigenous communities, on chronic disease prevention for Hispanic and Latino populations, and a community-driven effort to leverage AI/ML to improve allocation of resources focused on social determinants of health for Native Hawaiians. This work aims to provide applied examples of how community-engagement has been incorporated into AI/ML development and implementation, with the goal of encouraging those in the prevention science field to consider the voices of the community as the use of such tools grows. Engaging with the community around AI/ML is critical to ensure these tools reach populations in need and advance health equity for all.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1227-1238"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12865688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145967414","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":"Detecting Patterns of Intimate Partner Violence Using Qualitative Analyses and Machine Learning Algorithms.","authors":"Ying Zhang, Jun Fang, Ambika Krishnakumar","doi":"10.1007/s11121-026-01923-1","DOIUrl":"10.1007/s11121-026-01923-1","url":null,"abstract":"<p><p>Intimate partner violence (IPV) survivors increasingly use social media platforms to share their experiences and to seek help and support for their IPV-related concerns. IPV evidence extracted from social media platforms can provide valuable information and complement data obtained from conventional data sources (e.g., self-reports and interviews) thereby enhancing our understanding of IPV victimization. This study addressed three research questions: (1) What range of IPV behaviors emerge through qualitative coding? (2) To what extent do machine learning (ML) based text classifications yield results comparable to qualitative coding of IPV behaviors? and (3) Do the conceptualizations that emerge from unsupervised ML capture additional behaviors or contextual information not identified through qualitative analyses? We analyzed 400 posts from women on IPV-related online forums using qualitative content analysis and two ML approaches: supervised text classification and unsupervised topic modeling (Latent Dirichlet Allocation). Supervised learning approaches, notably Random Forest and Neural Networks, proved effective in classifying IPV violence subtypes with high accuracy (F1 scores .62 - .85). A comparison of findings from the qualitative and topic modeling approaches supported the presence of distinct characteristics of IPV: physical and sexual violence, psychological/emotional abuse, and coercive control. The ML model revealed vocabulary patterns consistent with relational and child-related contexts, temporal and frequency indicators of violence, references to legal system engagement, and spatial contexts, elements that were less captured through thematic qualitative coding alone. The consistency of findings across qualitative and ML approaches points to the potential of leveraging ML techniques when analyzing qualitative data, thus enabling the development of timely and effective IPV interventions.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1176-1190"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550090/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147844579","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":"AI-Augmented Clearinghouse to Facilitate Evidence-Based Decision-Making and Social Spending: a Conceptual Framework.","authors":"Pamela R Buckley, Diana Fishbein, Neil J Wollman","doi":"10.1007/s11121-025-01848-1","DOIUrl":"10.1007/s11121-025-01848-1","url":null,"abstract":"<p><p>Social spending refers to public or private funds aimed at helping vulnerable groups. Evidence-based decision-making supports social spending by applying existing evidence to inform funding and involves integrating relevant information from various sources, such as experimental studies and community feedback. Online clearinghouses support evidence-based decision-making by synthesizing evidence on what works, though manually updating literature is incomplete. In addition, passively summarizing evaluations is insufficient for end-users to implement preventive solutions that achieve population impacts. A responsive platform and a dissemination plan are needed to encourage the uptake of equitable and culturally relevant preventive interventions grounded in transparency and rigorous evidence of effectiveness. A two-part conceptual framework is proposed for a clearinghouse platform that integrates stepwise, interactive, AI-driven capabilities safeguarded by human touchpoints. First, clearinghouses should adopt a \"living\" review to automatically update evaluations-an approach embraced globally by organizations such as the World Health Organization, Cochrane Collaboration, and the U.N.'s Pan American Health Organization. The second component involves adding a chatbot to support assessment and implementation guidance and make recommendations on (1) the provision of all evidence-based preventive interventions (EBPIs) and their key activities, (2) EBPIs shown to achieve equitably distributed outcomes, (3) culturally relevant EBPIs that align with the values and needs of a target population, (4) implementation support, such as materials, training, and fidelity measures, and (5) delivery costs. The resulting platform will ethically expedite the translational process of identifying and scaling EBPIs, leading to a more complete, comprehensive, and accessible body of evidence on effective preventive strategies.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1203-1215"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145276381","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":"Improving Community-Based Care for Adolescents with ADHD: a Randomized Controlled Trial of Artificial Intelligence-Assisted Fidelity Supports.","authors":"Margaret H Sibley, Leonard Bickman, David Atkins, Stefany Coxe, Julian King, Michael Tanana, Pablo Martin, Timothy F Page, Mercedes Ortiz, Joshua Tapia, Ashley Sparber, Xin Zhao","doi":"10.1007/s11121-025-01868-x","DOIUrl":"10.1007/s11121-025-01868-x","url":null,"abstract":"<p><p>Cognitive-behavioral treatments (CBTs) for adolescents with ADHD demonstrate promise of long-term effects on outcome. However, their implementation in routine care community clinics faces barriers that impact quantity, efficiency, and quality of delivery, as well as client outcomes. This study is a randomized controlled trial designed to evaluate the impact of an AI-assisted service delivery model on therapist implementation of Supporting Teens' Autonomy Daily (STAND), a CBT blended with Motivational Interviewing (MI) for adolescents with ADHD. Adolescents with ADHD (N = 51), who were clients at three community mental health agencies, received treatment from 23 therapists. There was randomization of adolescents and therapists to AI-assisted or standard implementation supports. In addition to standard supports (i.e., training, standard facilitation resources, technical assistance, case supervision), AI-assisted support package included digitized facilitation resources housed in a clinical dashboard (Care4), feedback on content fidelity, and AI-generated feedback on MI implementation quality. The AI-assisted group was associated with more efficient treatment delivery and lower number of appointments attended by the adolescent. There was also a significant decrement in MI quality over time in the AI-assisted group compared to the standard support group. Feedback in focus groups indicated that therapists perceived a task-oriented mindset to be associated with receipt of the AI-assisted support package, leading therapists to prioritize efficiency over relational aspects of therapy. Following the results of this trial, a future, larger RCT should examine the impact of the AI-assisted implementation model on mental health outcomes and cost savings to organizations, third party payers, and clients. Trial registration number: NCT05135065; https://www.clinicaltrials.gov ; Registered September 2021.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1099-1110"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145821695","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":"Introduction to the Special Issue on Transforming Prevention Science: Advances and Applications of Artificial Intelligence and Related Methods.","authors":"Emily E Haroz, Catherine P Bradshaw, Daniel J Feaster","doi":"10.1007/s11121-026-01971-7","DOIUrl":"10.1007/s11121-026-01971-7","url":null,"abstract":"<p><p>Artificial intelligence (AI) has had an unprecedented impact on several aspects of the human condition and is increasingly being incorporated into research endeavors. AI and related research methods hold great promise for the prevention science field. This special issue, \"Transforming Prevention Science: Advances and Applications of Artificial Intelligence and Related Methods,\" features several examples of the application of, and technical development in, AI and other research methods and technologies. The current paper introduces this special issue, which was launched through an open call issued in August 2024. The goal of the special issue was to bring together original empirical, methodological, and conceptual papers on the use of AI and machine learning in prevention research and practice. The papers are organized into four cross-cutting themes: (a) use of AI for prevention-relevant risk stratification across universal, selective, and indicated prevention; (b) use of AI to improve prevention services, programs, and intervention delivery; (c) use of AI as a research methodology and tool; and (d) frameworks, ethics, equity, and infrastructure for responsible AI use in prevention science. Equity, community engagement, and methodological transparency cut across all four themes. This introductory paper closes with a discussion of cross-cutting methodological and measurement considerations and priorities for the field, including the implications of large language models for prevention research conducted during and after the issue's submission window.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1015-1022"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867325","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}