Mayo Clinic Proceedings. Digital health最新文献

{"title":"Real-World Assessment of the Efficacy of Computer-Assisted Diagnosis in Colonoscopy: A Single Institution Cohort Study in Singapore","authors":"Gabrielle E. Koh MBBS ,&nbsp;Brittany Ng MBBS ,&nbsp;Ronja M.B. Lagström MSc ,&nbsp;Fung-Joon Foo FRCS ,&nbsp;Shuen-Ern Chin MBBS ,&nbsp;Fang-Ting Wan MBBS ,&nbsp;Juinn Huar Kam FRCS ,&nbsp;Baldwin Yeung PhD, FRCS ,&nbsp;Clarence Kwan MRCP ,&nbsp;Cesare Hassan MD, PhD ,&nbsp;Ismail Gögenur MD, DMSc ,&nbsp;Frederick H. Koh FRCS, PhD","doi":"10.1016/j.mcpdig.2024.10.002","DOIUrl":"10.1016/j.mcpdig.2024.10.002","url":null,"abstract":"<div><h3>Objective</h3><div>To review the efficacy and accuracy of the GI Genius Intelligent Endoscopy Module Computer-Assisted Diagnosis (CADx) program in colonic adenoma detection and real-time polyp characterization.</div></div><div><h3>Patients and Methods</h3><div>Colonoscopy remains the gold standard in colonic screening and evaluation. The incorporation of artificial intelligence (AI) technology therefore allows for optimized endoscopic performance. However, validation of most CADx programs with real-world data remains scarce. This prospective cohort study was conducted within a single Singaporean institution between April 1, 2023 and December 31, 2023. Videos of all AI-enabled colonoscopies were reviewed with polyp-by-polyp analysis performed. Real-time polyp characterization predictions after sustained polyp detection were compared against final histology results to assess the accuracy of the CADx system at colonic adenoma identification.</div></div><div><h3>Results</h3><div>A total of 808 videos of CADx colonoscopies were reviewed. Out of the 781 polypectomies performed, 543 (69.5%) and 222 (28.4%) were adenomas and non-adenomas on final histology, respectively. Overall, GI Genius correctly characterized adenomas with 89.4% sensitivity, 61.7% specificity, a positive predictive value of 85.4%, a negative predictive value of 69.8%, and 81.5% accuracy. The negative predictive value for rectosigmoid lesions (80.3%) was notably higher than for colonic lesions (54.2%), attributed to the increased prevalence of hyperplastic rectosigmoid polyps (11.4%) vs other colonic regions (5.4%).</div></div><div><h3>Conclusion</h3><div>Computer-Assisted Diagnosis is therefore a promising adjunct in colonoscopy with substantial clinical implications. Accurate identification of low-risk non-adenomatous polyps encourages the adoption of “resect-and-discard” strategies. However, further calibration of AI systems is needed before the acceptance of such strategies as the new standard of care.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 647-655"},"PeriodicalIF":0.0,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704499","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 Large Language Model–Supported Instructions for Medication Use: First Steps Toward a Comprehensive Model","authors":"Zilma Silveira Nogueira Reis MD, PhD ,&nbsp;Adriana Silvina Pagano MA, PhD ,&nbsp;Isaias Jose Ramos de Oliveira MSc ,&nbsp;Cristiane dos Santos Dias MD, PhD ,&nbsp;Eura Martins Lage MD, PhD ,&nbsp;Erico Franco Mineiro PhD ,&nbsp;Glaucia Miranda Varella Pereira PhD ,&nbsp;Igor de Carvalho Gomes MSc ,&nbsp;Vinicius Araujo Basilio MS ,&nbsp;Ricardo João Cruz-Correia PhD ,&nbsp;Davi dos Reis de Jesus BCS ,&nbsp;Antônio Pereira de Souza Júnior MS ,&nbsp;Leonardo Chaves Dutra da Rocha PhD","doi":"10.1016/j.mcpdig.2024.09.006","DOIUrl":"10.1016/j.mcpdig.2024.09.006","url":null,"abstract":"<div><h3>Objective</h3><div>To assess the support of large language models (LLMs) in generating clearer and more personalized medication instructions to enhance e-prescription.</div></div><div><h3>Patients and Methods</h3><div>We established patient-centered guidelines for adequate, acceptable, and personalized directions to enhance e-prescription. A dataset comprising 104 outpatient scenarios, with an array of medications, administration routes, and patient conditions, was developed following the Brazilian national e-prescribing standard. Three prompts were submitted to a closed-source LLM. The first prompt involved a generic command, the second one was calibrated for content enhancement and personalization, and the third one requested bias mitigation. The third prompt was submitted to an open-source LLM. Outputs were assessed using automated metrics and human evaluation. We conducted the study between March 1, 2024 and September 10, 2024.</div></div><div><h3>Results</h3><div>Adequacy scores of our closed-source LLM’s output showed the third prompt outperforming the first and second one. Full and partial acceptability was achieved in 94.3% of texts with the third prompt. Personalization was rated highly, especially with the second and third prompts. The 2 LLMs showed similar adequacy results. Lack of scientific evidence and factual errors were infrequent and unrelated to a particular prompt or LLM. The frequency of hallucinations was different for each LLM and concerned prescriptions issued upon symptom manifestation and medications requiring dosage adjustment or involving intermittent use. Gender bias was found in our closed-source LLM’s output for the first and second prompts, with the third one being bias-free. The second LLM’s output was bias-free.</div></div><div><h3>Conclusion</h3><div>This study demonstrates the potential of LLM-supported generation to produce prescription directions and improve communication between health professionals and patients within the e-prescribing system.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 632-644"},"PeriodicalIF":0.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704498","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":"Application of Federated Learning in Cardiology: Key Challenges and Potential Solutions","authors":"Md Saifur Rahman PhD ,&nbsp;Chandan Karmarkar PhD ,&nbsp;Sheikh Mohammed Shariful Islam MBBS, PhD","doi":"10.1016/j.mcpdig.2024.09.005","DOIUrl":"10.1016/j.mcpdig.2024.09.005","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 590-595"},"PeriodicalIF":0.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552432","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":"Assessments of Generative Artificial Intelligence as Clinical Decision Support Ought to be Incorporated Into Randomized Controlled Trials of Electronic Alerts for Acute Kidney Injury","authors":"Donal J. Sexton MD, PhD ,&nbsp;Conor Judge MB, PhD","doi":"10.1016/j.mcpdig.2024.09.004","DOIUrl":"10.1016/j.mcpdig.2024.09.004","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 606-610"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592645","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":"Deep Learning Model for Predicting Neurodevelopmental Outcome in Very Preterm Infants Using Cerebral Ultrasound","authors":"Tahani M. Ahmad MD, ABR ,&nbsp;Alessandro Guida PhD ,&nbsp;Sam Stewart PhD ,&nbsp;Noah Barrett MSc ,&nbsp;Michael J. Vincer MD ,&nbsp;Jehier K. Afifi MD, MSc","doi":"10.1016/j.mcpdig.2024.09.003","DOIUrl":"10.1016/j.mcpdig.2024.09.003","url":null,"abstract":"<div><h3>Objective</h3><div>To develop deep learning (DL) models applied to neonatal cranial ultrasound (CUS) and clinical variables to predict neurodevelopmental impairment (NDI) in very preterm infants (VPIs) at 3 years of corrected age.</div></div><div><h3>Patients and Methods</h3><div>This is a retrospective study of a cohort of VPI (22⁰-30⁶ weeks’ gestation) born between 2004 and 2016 in Nova Scotia, Canada. Clinical data at hospital discharge and CUS images at 3 time points were used to develop DL models using elastic net (EN) and convolutional neural network (CNN). The models’ performances were compared using precision recall area under the curve (PR-AUC) and area under the receiver operation characteristic curve (ROC-AUC) with their 95% ci.</div></div><div><h3>Results</h3><div>Of 665 eligible VPIs, 619 (93%) infants with 4184 CUS images were included. The CNN model combining CUS and clinical variables reported better performance (PR-AUC, 0.75; 95% CI, 072-0.79; ROC-AUC, 0.71; 95% CI, 0.67-0.74) in the prediction of positive NDI outcome compared with the traditional models based solely on clinical predictors (PR-AUC, 0.60; 95% CI, 0.52-0.68; ROC-AUC, 0.72; 95% CI, 0.68-0.75). When analyzed by the CUS plane and acquisition time point, the model using the anterior coronal plane at 6 weeks of age provided the highest predictive accuracy (PR-AUC, 0.81; 95% CI, 0.77-0.91; ROC-AUC, 0.78; 95% CI, 0.66-0.87).</div></div><div><h3>Conclusion</h3><div>We developed and internally validated a DL prognostic model using CUS and clinical predictors to predict NDI in VPIs at 3 years of age. Early and accurate identification of infants at risk for NDI enables referral to targeted interventions, which improves functional outcomes.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 596-605"},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552431","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":"Medication Extraction and Drug Interaction Chatbot: Generative Pretrained Transformer-Powered Chatbot for Drug-Drug Interaction","authors":"Won Tae Kim MD, PhD ,&nbsp;Jaegwang Shin ,&nbsp;In-Sang Yoo ,&nbsp;Jae-Woo Lee MD, PhD ,&nbsp;Hyun Jeong Jeon MD, PhD ,&nbsp;Hyo-Sun Yoo MD ,&nbsp;Yongwhan Kim MD ,&nbsp;Jeong-Min Jo ,&nbsp;ShinJi Hwang ,&nbsp;Woo-Jeong Lee ,&nbsp;Seung Park PhD ,&nbsp;Yong-June Kim MD, PhD","doi":"10.1016/j.mcpdig.2024.09.001","DOIUrl":"10.1016/j.mcpdig.2024.09.001","url":null,"abstract":"<div><h3>Objective</h3><div>To assist individuals, particularly cancer patients or those with complex comorbidities, in quickly identifying potentially contraindicated medications when taking multiple drugs simultaneously.</div></div><div><h3>Patients and Methods</h3><div>In this study, we introduce the Medication Extraction and Drug Interaction Chatbot (MEDIC), an artificial intelligence system that integrates optical character recognition and Chat generative pretrained transformer through the Langchain framework. Medication Extraction and Drug Interaction Chatbot starts by receiving 2 drug bag images from the patient. It uses optical character recognition and text similarity techniques to extract drug names from the images. The extracted drug names are then processed through Chat generative pretrained transformer and Langchain to provide the user with information about drug contraindications. The MEDIC responds to the user with clear and concise sentences to ensure the information is easily understandable. This research was conducted from July 1, 2022 to April 30, 2024.</div></div><div><h3>Results</h3><div>This streamlined process enhances the accuracy of drug-drug interaction detection, providing a crucial tool for health care professionals and patients to improve medication safety. The proposed system was validated through rigorous evaluation using real-world data, reporting high accuracy in drug-drug interaction identification and highlighting its potential to benefit medication management practices considerably.</div></div><div><h3>Conclusion</h3><div>By implementing MEDIC, contraindicated medications can be identified using only medication packaging, and users can be alerted to potential drug adverse effects, thereby contributing to advancements in patient care in clinical settings.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 611-619"},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650827","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":"Use of a Head-Mounted Assisted Reality, High-Resolution Telemedicine Camera and Satellite Communication Terminal in an Out-of-Hospital Cardiac Arrest","authors":"Christopher S. Russi DO ,&nbsp;Sarayna S. McGuire MD ,&nbsp;Aaron B. Klassen MD ,&nbsp;Kate M. Skeens MD ,&nbsp;Kate J. Arms NREMT-P ,&nbsp;Lindsey D. Kaczmerick NREMT-P ,&nbsp;Patrick J. Fullerton DO, MHCM ,&nbsp;Louis M. Radnothy DO ,&nbsp;Anuradha Luke MD","doi":"10.1016/j.mcpdig.2024.09.002","DOIUrl":"10.1016/j.mcpdig.2024.09.002","url":null,"abstract":"<div><div>Mayo Clinic Ambulance Service is testing a novel combination of technologies to enhance the ability to provide prehospital telemedicine connecting physicians with paramedics. Mayo Clinic Ambulance Service partnered with start-up company OPTAC-X to field test a novel head-mounted video camera connected with a satellite communications terminal to bring medical control emergency medicine physicians to the patient and paramedic by video. The authors believe this is the first report of a physician providing medical guidance to paramedics resuscitating an out-of-hospital cardiac arrest using these technologies.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 584-589"},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529549","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":"Challenges and Limitations of Human Oversight in Ethical Artificial Intelligence Implementation in Health Care: Balancing Digital Literacy and Professional Strain","authors":"Roanne van Voorst PhD","doi":"10.1016/j.mcpdig.2024.08.004","DOIUrl":"10.1016/j.mcpdig.2024.08.004","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 559-563"},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000798/pdfft?md5=b1d9e371d52b9b7e6fc864fafe9eab4c&pid=1-s2.0-S2949761224000798-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311132","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":"Embedding Internal Accountability Into Health Care Institutions for Safe, Effective, and Ethical Implementation of Artificial Intelligence Into Medical Practice: A Mayo Clinic Case Study","authors":"Brenna Loufek MS ,&nbsp;David Vidal JD ,&nbsp;David S. McClintock MD ,&nbsp;Mark Lifson PhD ,&nbsp;Eric Williamson MD ,&nbsp;Shauna Overgaard PhD ,&nbsp;Kathleen McNaughton JD ,&nbsp;Melissa C. Lipford MD ,&nbsp;Darrell S. Pardi MD","doi":"10.1016/j.mcpdig.2024.08.008","DOIUrl":"10.1016/j.mcpdig.2024.08.008","url":null,"abstract":"<div><div>Health care organizations are building, deploying, and self-governing digital health technologies (DHTs), including artificial intelligence, at an increasing rate. This scope necessitates expertise and quality infrastructure to ensure that the technology impacting patient care is safe, effective, and ethical throughout its lifecycle. The objective of this article is to describe Mayo Clinic’s approach for embedding internal accountability as a case study for other health care institutions seeking modalities for responsible implementation of artificial intelligence–enabled DHTs. Mayo Clinic aims to enable and empower innovators by (1) building internal skills and expertise, (2) establishing a centralized review board, and (3) aligning development and deployment processes with regulations, standards, and best practices. In 2022, Mayo Clinic established the Software as a Medical Device Review Board (The Board), an independent body of physicians and domain experts to represent the organization in providing innovators regulatory and risk mitigation recommendations for DHTs. Hundreds of digital health product teams have since benefited from this function, intended to enable responsible innovation in alignment with regulation and state-of-the-art quality management practices. Other health care institutions can adopt similar internal accountability bodies using this framework. Opportunity remains to iterate on Mayo Clinic’s approach in alignment with advancing best practices and enhance representation on The Board as part of standard continuous improvement practices.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 574-583"},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419546","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":"Echocardiographic Diagnosis of Hypertrophic Cardiomyopathy by Machine Learning","authors":"Nasibeh Zanjirani Farahani PhD ,&nbsp;Mateo Alzate Aguirre MD ,&nbsp;Vanessa Karlinski Vizentin MD ,&nbsp;Moein Enayati PhD ,&nbsp;J. Martijn Bos MD, PhD ,&nbsp;Andredi Pumarejo Medina MD ,&nbsp;Kathryn F. Larson MD ,&nbsp;Kalyan S. Pasupathy PhD ,&nbsp;Christopher G. Scott MS ,&nbsp;April L. Zacher MS ,&nbsp;Eduard Schlechtinger MS ,&nbsp;Bruce K. Daniels RDCS ,&nbsp;Vinod C. Kaggal MS ,&nbsp;Jeffrey B. Geske MD ,&nbsp;Patricia A. Pellikka MD ,&nbsp;Jae K. Oh MD ,&nbsp;Steve R. Ommen MD ,&nbsp;Garvan C. Kane MD ,&nbsp;Michael J. Ackerman MD, PhD ,&nbsp;Adelaide M. Arruda-Olson MD, PhD","doi":"10.1016/j.mcpdig.2024.08.009","DOIUrl":"10.1016/j.mcpdig.2024.08.009","url":null,"abstract":"<div><h3>Objective</h3><div>To develop machine learning tools for automated hypertrophic cardiomyopathy (HCM) case recognition from echocardiographic metrics, aiming to identify HCM from standard echocardiographic data with high performance.</div></div><div><h3>Patients and Methods</h3><div>Four different random forest machine learning models were developed using a case-control cohort composed of 5548 patients with HCM and 16,973 controls without HCM, from January 1, 2004, to March 15, 2019. Each patient with HCM was matched to 3 controls by sex, age, and year of echocardiography. Ten-fold crossvalidation was used to train the models to identify HCM. Variables included in the models were demographic characteristics (age, sex, and body surface area) and 16 standard echocardiographic metrics.</div></div><div><h3>Results</h3><div>The models were differentiated by global, average, individual, or no strain measurements. Area under the receiver operating characteristic curves (area under the curve) ranged from 0.92 to 0.98 for the 4 separate models. Area under the curves of model 2 (using left ventricular global longitudinal strain; 0.97; 95% CI, 0.95-0.98), 3 (using averaged strain; 0.96; 95% CI, 0.94-0.97), and 4 (using 17 individual strains per patient; 0.98; 95% CI, 0.97-0.99) had comparable performance. By comparison, model 1 (no strain data; 0.92; 95% CI, 0.90-0.94) had an inferior area under the curve.</div></div><div><h3>Conclusion</h3><div>Machine learning tools that analyze echocardiographic metrics identified HCM cases with high performance. Detection of HCM cases improved when strain data was combined with standard echocardiographic metrics.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 564-573"},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419545","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}