Journal of the American Medical Informatics Association最新文献

{"title":"Who is doing informatics work in US governmental public health agencies?","authors":"Sripriya Rajamani, Divya Rupini Gunashekar, Umesh Ghimire, Jonathon P Leider, Brian E Dixon","doi":"10.1093/jamia/ocag129","DOIUrl":"10.1093/jamia/ocag129","url":null,"abstract":"<p><strong>Objectives: </strong>Data modernization (DM) seeks to transform data and information systems in public health (PH) to support core activities. This study characterizes incumbent PH workers who perform informatics and data-centric roles that are critical to DM activities.</p><p><strong>Materials and methods: </strong>Responses from the 2024 Public Health Workforce Interests and Needs Survey (PH WINS) on the US governmental PH workforce were analyzed. Out of 77 PH job classifications, 9 were mapped to informatics/data-centric roles. Demographics, work characteristics, and contribution to informatics and other PH program areas of these roles were examined.</p><p><strong>Results: </strong>Based on weighted responses, informatics and data-centric roles comprise approximately 8% (N = 4786) of the PH workforce. This included PH informatics specialists (<1%), epidemiologists (3.9%), data analytics/related roles (1.7%), and information technology/computer science (IT/CS) workers (2.1%).</p><p><strong>Discussion: </strong>Informatics roles are performed by PH informatics specialists as well as other workers, like epidemiologists, and PH informatics specialists support a broad range of PH activities including surveillance.</p><p><strong>Conclusion: </strong>Achieving the goals of DM and the national Public Health Data Strategy will require developing and supporting a variety of informatics and data-centric roles in PH agencies.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2002-2007"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630290/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148632438","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":"Institutional approaches to artificial intelligence policy and guidance in health informatics and information management education: emerging trends and inconsistencies.","authors":"Christina Eldredge, Diane Dolezel, Josette Jones, Neha Vooppala, Stephen E Bronsburg","doi":"10.1093/jamia/ocag150","DOIUrl":"10.1093/jamia/ocag150","url":null,"abstract":"<p><strong>Objective: </strong>To examine the characteristics, scope, and thematic content of institutional and program-level academic artificial intelligence (AI) policies and guidance documents used by CAHIIM-accredited Health Informatics (HI) and Health Information Management (HIM) master's programs in the United States.</p><p><strong>Materials and methods: </strong>This convergent mixed‑methods study examined publicly available AI‑related documents (eg, student handbooks, integrity policies, program manuals) from all 48 CAHIIM‑accredited HI and HIM master's programs listed in November 2025. Forty programs (83%) had at least 1 qualifying document, 8 programs had no qualifying documents. Quantitative analyses summarized policy characteristics and examined associations with delivery modality using Fisher's exact tests. Qualitative analysis applied Latent Dirichlet Allocation topic modeling to identify recurring semantic themes across policy texts.</p><p><strong>Results: </strong>Most documents provided guidance rather than formal policies; neither policy type nor audience differed by delivery mode. Content emphasized academic integrity, responsible AI use, and expectations for student conduct. References to privacy, intellectual property, and regulatory concepts were present but less common than academic integrity-related themes. Topic modeling identified 4 dominant themes: academic integrity and appropriate AI use; generative AI in teaching; AI and data‑tool use in research; and student engagement with conversational AI systems.</p><p><strong>Conclusion: </strong>AI guidance and policy are available across most accredited HI and HIM master's programs; however, they vary in structure and scope. The majority of documents emphasize academic integrity and responsible AI use. Future research could examine how academic program AI policies relate to distinct but related domains such as curriculum development and organizational AI governance.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1984-1991"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148963832","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":"CUI-Curate: a GraphRAG-based framework for automated clinical concept curation for NLP applications.","authors":"Victoria Blake, Jamie Novak, Mathew Miller, Sze-Yuan Ooi, Blanca Gallego","doi":"10.1093/jamia/ocag110","DOIUrl":"10.1093/jamia/ocag110","url":null,"abstract":"<p><strong>Background: </strong>Clinical named entity recognition tools commonly map free text to Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs). For many downstream tasks, however, the clinically meaningful unit is not a single CUI but a concept set comprising related synonyms, subtypes, and associated concepts. Constructing these sets is labour-intensive, inconsistently performed, and poorly supported by existing tools.</p><p><strong>Methods: </strong>We present CUI-Curate, a graph-based retrieval-augmented-generation (GraphRAG) framework for automated UMLS concept set curation. A UMLS knowledge graph was constructed and embedded for semantic retrieval. Candidate CUIs were retrieved using graph-based expansion and then filtered and classified using large language models (GPT-5 and Qwen3-32B). The framework was evaluated on five lexically heterogeneous clinical concepts against manually curated concept sets and gold-standard concept sets.</p><p><strong>Results: </strong>CUI-Curate produced substantially larger and more complete concept sets than the manual benchmarks. A single retrieval configuration across concepts achieved high recall of definitive concepts with manageable candidate sets. GPT-5 outperformed manual curation for all concepts and retained at least 95% of definitive gold-standard CUIs, while Qwen3-32B achieved comparable but slightly lower performance. Many missed concepts were not observed in 10,000 MIMIC-III notes. CUI-Curate infrastructure and end-to-end processing were inexpensive and stable across runs.</p><p><strong>Conclusions: </strong>CUI-Curate offers a scalable, reproducible, and cost-efficient approach for generating clinician-reviewable UMLS concept sets tailored to clinical natural language processing and phenotyping applications.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1898-1909"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148334068","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":"CiteSure: retrieval-augmented large language models for faithful biomedical citation recommendation.","authors":"Qianqian Xie, Jeffrey Zhang, Yan Wang, Jimin Huang, Fongci Lin, Ruey-Ling Weng, Huan He, Qingyu Chen, Hua Xu","doi":"10.1093/jamia/ocag122","DOIUrl":"10.1093/jamia/ocag122","url":null,"abstract":"<p><strong>Objectives: </strong>Accurate citation of relevant publications is essential for scientific integrity in biomedical research. Large language models (LLMs) excel at text generation but often hallucinate fabricated or inaccurate citations. Retrieval-augmented generation (RAG) can mitigate these errors, yet current approaches lack semantic precision in evidence retrieval. This study aims to develop a domain-specific RAG system for reliable, context-specific biomedical citation recommendations.</p><p><strong>Materials and methods: </strong>We introduce CiteSure, a sentence-level citation recommendation tool designed to deliver reliable, evidence-based, and context-specific references using LLMs. CiteSure utilizes a 2-stage retrieval-augmented generation (RAG) framework, combining a domain-specific dense retriever (BioLLM2Vec) and reranker (BioRankLLaMA), adapted from LLaMA3-8B-Instruct using biomedical-specific training data. CiteSure leverages the complementary strengths of retrieval and generative LLM models, ensuring factual precision and contextual alignment. We evaluated CiteSure on a curated Alzheimer's disease dataset, comparing it to standalone LLMs and traditional retrieval-based methods.</p><p><strong>Results: </strong>CiteSure achieved 100% factual accuracy and the highest relevance score of 77.50%, outperforming all baselines. BioLLM2Vec retrieved relevant articles with over 80% accuracy in the top 100 candidates. BioRankLLaMA consistently outperformed baseline rerankers across MAP, MRR, and Precision@5 metrics, confirming the benefit of domain-specific adaptation and contrastive fine-tuning.</p><p><strong>Discussion and conclusion: </strong>Our results demonstrate that CiteSure, built on a 2-stage retrieval-augmented generation framework, effectively integrates domain-specific retrieval with LLM-based generation to achieve substantial improvements over baseline approaches. Our work underscores the importance of domain-specific adaptation in biomedical citation recommendation and provides publicly available datasets, models, and code for support future research.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1934-1942"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630303/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148702468","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":"PhenoSS: phenotype semantic similarity-based approach for rare disease prediction and patient clustering.","authors":"Shihan Chen, Quan M Nguyen, Yu Hu, Cong Liu, Chunhua Weng, Kai Wang","doi":"10.1093/jamia/ocag131","DOIUrl":"10.1093/jamia/ocag131","url":null,"abstract":"<p><strong>Objectives: </strong>Systematic clinical phenotyping using Human Phenotype Ontology (HPO) is central to rare disease diagnosis. However, current disease prioritization (ranking candidate diseases from HPO for a patient) methods face key challenges: they often fail to account for the hierarchical structure of HPO terms, ignore dependencies among correlated terms, and do not adjust for batch effects arising from systematic differences in phenotype documentation across cohorts, institutions, or clinicians. We aim to develop a scalable and statistically principled framework to address these limitations for rare disease prediction and patient stratification.</p><p><strong>Materials and methods: </strong>We developed PhenoSS, a Gaussian copula-based framework that models disease-specific marginal prevalence of HPO terms while capturing their joint dependencies through a multivariate normal distribution. Phenotype frequencies were estimated using external curated resources, including OARD (Open Annotations for Rare Diseases) and HPO annotations. PhenoSS supports both pair-wise phenotype similarity calculation for patient clustering and posterior odds estimation for patient-specific disease prioritization. A batch-effect correction module mitigates systematic phenotyping differences across datasets.</p><p><strong>Results: </strong>Across diverse simulation scenarios, PhenoSS demonstrated robust disease-prediction performance and consistently improved accuracy after batch-effect correction. In real electronic health record data, PhenoSS identified clinically meaningful patient clusters and effectively distinguished patients with different rare diseases. In disease prioritization tasks, PhenoSS achieved competitive performance with existing methods, particularly for patients exhibiting sparse or noisy phenotype annotations.</p><p><strong>Conclusion: </strong>PhenoSS provides a statistically interpretable framework for modeling phenotypic heterogeneity in rare disease research and is adaptable to other structured clinical vocabularies such as SNOMED-CT and ICD codes.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1967-1983"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630301/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148632485","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 and academic publishing.","authors":"Suzanne Bakken","doi":"10.1093/jamia/ocag159","DOIUrl":"https://doi.org/10.1093/jamia/ocag159","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":"33 10","pages":"1849-1851"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117792","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":"Equity-by-design for socially assistive robots as digital health tools.","authors":"Reza Kachouie, Belinda Barton","doi":"10.1093/jamia/ocag105","DOIUrl":"10.1093/jamia/ocag105","url":null,"abstract":"<p><strong>Objectives: </strong>To propose an equity-by-design agenda for socially assistive robots (SARs) as embodied digital health informatics interventions.</p><p><strong>Materials and methods: </strong>We developed this Perspective via a purposive narrative synthesis of SARs healthcare studies, digital health equity, informatics governance, and human-robot interaction ethics/equity, integrating care- and technology-ethics to derive a 3-level equity framework.</p><p><strong>Results: </strong>We outline equity requirements at product (user), institutional (meso), and policy/evaluation (macro) levels and operationalize them as a checklist covering co-design, accessibility, privacy, data governance, equitable access and financing, and equity-oriented evaluation.</p><p><strong>Discussion: </strong>Applying equity-by-design to SARs highlights how embodied sensing, workflow fit and organizational readiness, and governance/reimbursement incentives determine who benefits, which risks are borne, and whether deployments narrow or widen digital divides.</p><p><strong>Conclusion: </strong>Treating SARs as embodied informatics interventions and operationalizing equity across micro (product), meso (institution), and macro (policy) levels can guide designers, informatics teams, providers, and payers toward deployments that are safe, acceptable, and just.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2051-2060"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630291/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148340852","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":"Evaluating large-scale propensity score adjustment when sample size is small.","authors":"Fleur Vereijken, Jenna M Reps, Marc A Suchard, Linying Zhang, George Hripcsak, Peter R Rijnbeek, Ross D Williams, Martijn J Schuemie","doi":"10.1093/jamia/ocag133","DOIUrl":"10.1093/jamia/ocag133","url":null,"abstract":"<p><strong>Background: </strong>Large-scale propensity score (LSPS) models are increasingly used to control confounding in observational studies, but their reliability in small-sample settings is unclear. Small samples can limit a study's ability to estimate propensity scores accurately and achieve adequate covariate balance, raising concerns about insufficient confounding adjustment. Resulting in small datasets being excluded, despite potentially containing valid information.</p><p><strong>Methods: </strong>We evaluated the balancing and bias reduction performance of LSPS models using six target-comparator pairs. To emulate a distributed data network analysis setting, we partitioned two large real-world data sources into smaller subsets. Within each subset, LSPS models were fit locally and evaluated on covariate balance, treatment effect bias and precision after empirical calibration, using real negative controls and synthetic positive controls. Performance was compared to a global LSPS model trained on the full dataset.</p><p><strong>Results: </strong>Across most target-comparator pairs, LSPS models substantially reduced bias and, when applying empirical calibration, improved precision. PS matching was more resilient to small sample sizes than PS stratification, achieving adequate balance even at n = 500, while stratification sometimes failed at n = 4000. A slight bias increase was observed at the smallest sample sizes, though not universally. Standard balance diagnostics consistently failed below n = 20 000, while a recently proposed diagnostic accounting for chance imbalance did not.</p><p><strong>Conclusions: </strong>LSPS models generally provide reliable bias reduction in small-sample settings, supporting their use in federated analyses. However, standard balance diagnostics may be misleading in small samples, and alternatives should be considered, such as significance checking. When LSPS fails to reduce bias adequately, additional adjustment strategies are required.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1992-2001"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148671256","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":"Sustainable artificial intelligence in global health: balancing promise, equity, and environmental realities in low- and middle-income countries.","authors":"Yuri Quintana, Titus Schleyer, Arriel Benis, Hamish Fraser, Amanda L Joseph, Felix Holl, Elizabeth A Campbell","doi":"10.1093/jamia/ocag140","DOIUrl":"10.1093/jamia/ocag140","url":null,"abstract":"<p><strong>Objective: </strong>Artificial intelligence (AI) adoption in global health informatics is accelerating, yet scaling, sustainability, equity, and environmental challenges limit impact, particularly in Low and Middle Income Countries (LMICs).</p><p><strong>Materials and methods: </strong>Drawing on experience from members of the American Medical Informatics Association Global Health Informatics and Climate, Health and Informatics Working Groups, we synthesized implementation, evaluation, sustainability, and governance considerations for AI in resource constrained health systems.</p><p><strong>Results: </strong>We propose a framework integrating four components: Green AI necessity assessments; a One Digital Health systems lens; pragmatic, workflow integrated evaluation; and federated governance supporting locally led stewardship and cross institutional learning.</p><p><strong>Discussion: </strong>Sustainable AI requires moving beyond short term pilots to address infrastructure, environmental costs, workflow integration, equity, and locally relevant evidence.</p><p><strong>Conclusion: </strong>Funders and implementers should prioritize durable infrastructure, strengthened health information systems, rigorous evidence generation, environmentally responsible deployment, and local stewardship to achieve scalable, equitable, and sustainable AI in global health.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2061-2069"},"PeriodicalIF":7.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992349","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":"Evaluating the impact of social determinants of health on model performance and fairness in artificial intelligence for glaucoma progression prediction.","authors":"Sean Y T Lin, Rohith Ravindranath, Joshua D Stein, Tina Hernandez-Boussard, Sophia Y Wang","doi":"10.1093/jamia/ocag166","DOIUrl":"10.1093/jamia/ocag166","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the impact of incorporating community-level social determinants of health (SDoH) on the performance and algorithmic fairness of artificial intelligence (AI) models trained to predict glaucoma progression to surgery.</p><p><strong>Materials and methods: </strong>This multicenter study included 63 268 glaucoma patients across 12 centers. Using electronic health records and community-level SDoH features (Distressed Communities Index [DCI] and rural-urban commuting area [RUCA] codes), we developed XGBoost models to predict progression to incisional glaucoma surgery. We compared SDoH-unaware and SDoH-aware models across an internal test set and 3 diverse external test sets, evaluating area under the receiver operating characteristic curve (AUROC) (performance) and generalized equalized odds (GEO) (fairness).</p><p><strong>Results: </strong>Including SDoH features resulted in negligible changes in predictive performance across the internal and all external test sets. However, the SDoH-aware model improved algorithmic fairness (achieving lower GEO scores) in the internal set (0.098 vs 0.216) and a mixed-population external site (0.172 vs 0.378). Conversely, inclusion of SDoH marginally improved at a highly urbanized, low-DCI external site (0.491 vs 0.504) and failed to improve at a rural, high-DCI external site (0.241 vs 0.228).</p><p><strong>Discussion: </strong>Incorporating SDoH features can improve algorithmic fairness regarding socioeconomic disparities without sacrificing predictive accuracy; however, these benefits do not universally transfer to external sites with demographically dissimilar cohorts.</p><p><strong>Conclusion: </strong>Incorporating community-level SDoH features into glaucoma prediction models improved algorithmic fairness with respect to socioeconomic disparities, without significantly altering predictive accuracy. However, fairness benefits did not universally generalize to demographically dissimilar external cohorts. AI models require rigorous external validation across diverse populations to ensure equity prior to clinical implementation.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":7.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149082581","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}