Mayo Clinic Proceedings. Digital health最新文献

{"title":"","authors":"","doi":"10.1016/j.mcpdig.2024.11.007","DOIUrl":"10.1016/j.mcpdig.2024.11.007","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 1","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148331","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":"Gait Speed and Task Specificity in Predicting Lower-Limb Kinematics: A Deep Learning Approach Using Inertial Sensors","authors":"Vaibhav R. Shah MSc ,&nbsp;Philippe C. Dixon PhD","doi":"10.1016/j.mcpdig.2024.11.004","DOIUrl":"10.1016/j.mcpdig.2024.11.004","url":null,"abstract":"<div><h3>Objective</h3><div>To develop a deep learning framework to predict lower-limb joint kinematics from inertial measurement unit (IMU) data across multiple gait tasks (walking, jogging, and running) and evaluate the impact of dynamic time warping (DTW) on reducing prediction errors.</div></div><div><h3>Patients and Methods</h3><div>Data were collected from 18 participants fitted with IMUs and an optical motion capture system between May 25, 2023, and May 30, 2023. A long short-term memory autoencoder supervised regression model was developed. The model consisted of multiple long short-term memory and convolution layers. Acceleration and gyroscope data from the IMUs in 3 axes and their magnitude for the proximal and distal sensors of each joint (hip, knee, and ankle) were inputs to the model. Optical motion capture kinematics were considered ground truth and used as an output to train the prediction model.</div></div><div><h3>Results</h3><div>The deep learning models achieved a root-mean-square error of less than 6° for hip, knee, and ankle joint sagittal plane angles, with the ankle showing the lowest error (5.1°). Task-specific models reported enhanced performance during certain gait phases, such as knee flexion during running. The application of DTW significantly reduced root-mean-square error across all tasks by at least 3° to 4°. External validation of independent data confirmed the model’s generalizability.</div></div><div><h3>Conclusion</h3><div>Our findings underscore the potential of IMU-based deep learning models for joint kinematic predictions, offering a practical solution for remote and continuous biomechanical assessments in health care and sports science.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 1","pages":"Article 100183"},"PeriodicalIF":0.0,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149197","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":"The Effectiveness of Telehealth Intervention on Chronic Kidney Disease Management in Adults: A Systematic Review","authors":"Tess Ellis MS, RD,&nbsp;Anna J. Kwon MS,&nbsp;Mee Young Hong PhD","doi":"10.1016/j.mcpdig.2024.11.002","DOIUrl":"10.1016/j.mcpdig.2024.11.002","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate the effectiveness of telehealth programs on dietary habits, quality of life, renal function, and blood pressure in adults with chronic kidney disease (CKD).</div></div><div><h3>Patients and Methods</h3><div>A systematic literature review was completed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Using PubMed/Medline, Scopus, Embase, and ScienceDirect databases, articles published between 2012 and 2024 were selected using the following keywords: <em>telehealth</em>, <em>eHealth</em>, <em>mHealth</em>, <em>telemedicine</em>, <em>telenutrition</em>, and <em>chronic kidney disease</em>.</div></div><div><h3>Results</h3><div>A total of 13 studies—10 randomized controlled trials and 3 single-arm trials—were chosen for this review. In these trials, telehealth interventions were administered using mobile applications, phone calls, web-based communications, text messaging, wearable devices, or a combination of these tools to provide treatment for adults with CKD. Interdisciplinary collaboration between a dietitian and other health care team members was shown to improve renal function and dietary habits when providing telehealth interventions via mobile applications, phone calls, and text messaging. Web-based telehealth delivery that involves diverse health care personnel has been shown to improve the quality of life in adult patients with CKD.</div></div><div><h3>Conclusion</h3><div>Receiving treatment using telehealth communication methods may be a beneficial option for adult patients with CKD by enhancing accessibility, promoting multidisciplinary collaboration, and effectively managing blood pressure and dietary habits, leading to improved quality of life for patients. Future research administering homogeneous and rigorously controlled experimental methods with larger and more diverse populations, as well as longer study durations, is necessary to further elucidate the effectiveness of CKD treatment delivery via telehealth for adult patients.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 1","pages":"Article 100181"},"PeriodicalIF":0.0,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149195","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":"Digital Medicine Tools and the Work of Being a Patient: A Qualitative Investigation of Digital Treatment Burden in Patients With Diabetes","authors":"Misk A. Al Zahidy MS ,&nbsp;Sue Simha BS ,&nbsp;Megan Branda MS ,&nbsp;Mariana Borras-Osorio MD ,&nbsp;Maeva Haemmerle ,&nbsp;Viet-Thi Tran MD, PhD ,&nbsp;Jennifer L. Ridgeway PhD ,&nbsp;Victor M. Montori MD","doi":"10.1016/j.mcpdig.2024.11.001","DOIUrl":"10.1016/j.mcpdig.2024.11.001","url":null,"abstract":"<div><h3>Objective</h3><div>To understand the contribution of digital medicine tools (eg, continuous glucose monitoring systems, scheduling, and messaging applications) to treatment burden in patients with diabetes.</div></div><div><h3>Patients and Methods</h3><div>Between October and November 2023, we invited patients with type 1 or type 2 diabetes to participate in semistructured interviews. The interviewees completed the Treatment Burden Questionnaire as they reflected on how digital medicine tools affect their daily routines. A published taxonomy of treatment burden guided the qualitative content analysis of interview transcripts.</div></div><div><h3>Results</h3><div>In total, 20 patients agreed to participate and completed interviews (aged 21-77 years, 55% female, 60% living with type 2 diabetes). We found 5 categories of tasks related to the use of digital medicine tools that patients had to complete (eg, calibrating continuous glucose monitors), 3 factors that made these tasks burdensome (eg, cost of device replacements), and 2 categories of consequences of burdensome tasks on patient wellbeing (eg, fatigue from device alarms).</div></div><div><h3>Conclusion</h3><div>Patients identified how digital medicine tools contribute to their treatment burden. The resulting digital burden taxonomy can be used to inform the design, implementation, and prescription of digital medicine tools including support for patients as they normalize them in their lives.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 1","pages":"Article 100180"},"PeriodicalIF":0.0,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149198","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":"Strategic Considerations for Selecting Artificial Intelligence Solutions for Institutional Integration: A Single-Center Experience","authors":"Janice L. Pascoe BRMP ,&nbsp;Luqing Lu MS ,&nbsp;Matthew M. Moore MFA ,&nbsp;Daniel J. Blezek PhD ,&nbsp;Annie E. Ovalle BS ,&nbsp;Jane A. Linderbaum APRN, CNP ,&nbsp;Matthew R. Callstrom MD, PhD ,&nbsp;Eric E. Williamson MD","doi":"10.1016/j.mcpdig.2024.10.004","DOIUrl":"10.1016/j.mcpdig.2024.10.004","url":null,"abstract":"<div><div>Artificial intelligence (AI) promises to revolutionize health care. Early identification of disease, appropriate test selection, and automation of repetitive tasks are expected to optimize cost-effective care delivery. However, pragmatic selection and integration of AI algorithms to enable this transformation remain challenging. Health care leaders must navigate complex decisions regarding AI deployment, considering factors such as cost of implementation, benefits to patients and providers, and institutional readiness for adoption. A successful strategy needs to align AI adoption with institutional priorities, select appropriate algorithms to be purchased or internally developed, and ensure adequate support and infrastructure. Further, successful deployment requires algorithm validation and workflow integration to ensure efficacy and usability. User-centric design principles and usability testing are critical for AI adoption, ensuring seamless integration into clinical workflows. Once deployed, continuous improvement processes and ongoing algorithm support ensure continuous benefits to the clinical practice. Vigilant planning and execution are necessary to navigate the complexities of AI implementation in the health care environment. By applying the framework outlined in this article, institutions can navigate the ever-evolving and complex environment of AI in health care to maximize the benefits of these innovative technologies.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 665-676"},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721288","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":"Reviewers for Mayo Clinic Proceedings: Digital Health (2024)","authors":"","doi":"10.1016/j.mcpdig.2024.10.005","DOIUrl":"10.1016/j.mcpdig.2024.10.005","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 645-646"},"PeriodicalIF":0.0,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704497","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 Blueprint for Clinical-Driven Medical Device Development: The Feverkidstool Application to Identify Children With Serious Bacterial Infection","authors":"Evelien B. van Kempen MD ,&nbsp;Sanne E.W. Vrijlandt MD ,&nbsp;Kelly van der Geest MSc ,&nbsp;Sophie Lotgering MSc ,&nbsp;Tom A. Hueting PhD ,&nbsp;Rianne Oostenbrink MD, PhD","doi":"10.1016/j.mcpdig.2024.10.003","DOIUrl":"10.1016/j.mcpdig.2024.10.003","url":null,"abstract":"<div><div>Clinical decision rules (CDRs) integrated into applications enhance diagnostic and treatment prediction support for clinicians, necessitating Confirmité Europeenne (CE)-mark certification to enter the European market. We describe the development of a CDR as a medical device, focusing on challenges from a physician’s perspective exemplified by the Feverkidstool (FKT), a validated CDR for febrile children. We pursued a local process, aligned with the CE-marking process, to develop the FKT as in-house developed device. We aimed to provide a blueprint for colleagues. Medical device development, conforming the medical device regulation and performed by a multidisciplinary team, encompassed 5 stages: market scan, design, production, verification and validation and conformity assessment. Regulatory processes were continuously updated. The market scan identified a need for the FKT compared with existing applications. A prototype was designed in stage 2, further adjusted and improved based on the qualitative and quantitative results of stages 2-4. Lastly, stage 5 confirmed FKT’s performance and safety. Medical device development presents challenges for physicians, requiring collaboration for technical, regulatory, and financial expertise. Multidisciplinary teamwork also poses challenges, including uncertainties regarding responsibility and timelines. After CE certification, adapting to evolving needs and ensuring data privacy highlights the ongoing nature of medical device development.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 656-664"},"PeriodicalIF":0.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703869","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":"Cost-Effectiveness of Artificial Intelligence-Enabled Electrocardiograms for Early Detection of Low Ejection Fraction: A Secondary Analysis of the Electrocardiogram Artificial Intelligence-Guided Screening for Low Ejection Fraction Trial","authors":"Viengneesee Thao PhD, MS ,&nbsp;Ye Zhu MD, MPH, PhD ,&nbsp;Andrew S. Tseng MD, MPH ,&nbsp;Jonathan W. Inselman MS ,&nbsp;Bijan J. Borah PhD ,&nbsp;Rozalina G. McCoy MD, MS ,&nbsp;Zachi I. Attia PhD ,&nbsp;Francisco Lopez-Jimenez MD, MBA ,&nbsp;Patricia A. Pellikka MD ,&nbsp;David R. Rushlow MD, MBOE ,&nbsp;Paul A. Friedman MD ,&nbsp;Peter A. Noseworthy MD, MBA ,&nbsp;Xiaoxi Yao MPH, MS, PhD","doi":"10.1016/j.mcpdig.2024.10.001","DOIUrl":"10.1016/j.mcpdig.2024.10.001","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate the cost-effectiveness of using artificial intelligence (AI) to screen for low ejection fraction (EF) in routine clinical practice using a pragmatic randomized controlled trial (RCT).</div></div><div><h3>Patients and Methods</h3><div>In a post hoc analysis of the electrocardiogram (ECG) AI-guided screening for low ejection fraction trial, we developed a decision analytic model for patients aged 18 years and older without previously diagnosed heart failure and underwent a clinically indicated ECG between August 5, 2019, and March 31, 2020. In the previously published RCT, the intervention arm underwent an AI-guided targeted screening program for low EF with a workflow embedded into routine clinical practice—AI was applied to the ECG to identify patients at high-risk and recommended clinicians to order an ECG and the control arm received usual care without the screening program. We used results from the RCT for rates of low EF diagnosis and a lifetime Markov model to project the long-term outcomes. Quality-adjusted life years (QALYs), costs of intervention and treatment, disease event costs, incremental cost-effectiveness ratio (ICER), and cost for the number needed to screen. Multiple scenario and sensitivity analyses were performed.</div></div><div><h3>Results</h3><div>Compared with usual care, AI-integrated ECG was cost effective, with an incremental cost-effectiveness ratio of $27,858/QALY. The program remained cost effective even with a change in patient age and follow-up time duration, although the specific ICER values varied for these parameters. The program was more cost effective in outpatient settings (ICER $1651/QALY) than in inpatient or emergency room settings.</div></div><div><h3>Conclusion</h3><div>Implementing AI-guided targeted screening for low EF in routine clinical practice was cost effective.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 620-631"},"PeriodicalIF":0.0,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650837","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":"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}