PLOS digital health最新文献

{"title":"Implicit versus explicit Bayesian priors for epistemic uncertainty estimation in clinical decision support.","authors":"Malte Blattmann, Adrian Lindenmeyer, Stefan Franke, Thomas Neumuth, Daniel Schneider","doi":"10.1371/journal.pdig.0000801","DOIUrl":"10.1371/journal.pdig.0000801","url":null,"abstract":"<p><p>Deep learning models offer transformative potential for personalized medicine by providing automated, data-driven support for complex clinical decision-making. However, their reliability degrades on out-of-distribution inputs, and traditional point-estimate predictors can give overconfident outputs even in regions where the model has little evidence. This shortcoming highlights the need for decision-support systems that quantify and communicate per-query epistemic (knowledge) uncertainty. Approximate Bayesian deep learning methods address this need by introducing principled uncertainty estimates over the model's function. In this work, we compare three such methods on the task of predicting prostate cancer-specific mortality for treatment planning, using data from the PLCO cancer screening trial. All approaches achieve strong discriminative performance (AUROC = 0.86) and produce well-calibrated probabilities in-distribution, yet they differ markedly in the fidelity of their epistemic uncertainty estimates. We show that implicit functional-prior methods-specifically neural network ensembles and factorized weight prior variational Bayesian neural networks-exhibit reduced fidelity when approximating the posterior distribution and yield systematically biased estimates of epistemic uncertainty. By contrast, models employing explicitly defined, distance-aware priors-such as spectral-normalized neural Gaussian processes (SNGP)-provide more accurate posterior approximations and more reliable uncertainty quantification. These properties make explicitly distance-aware architectures particularly promising for building trustworthy clinical decision-support tools.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000801"},"PeriodicalIF":7.7,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12306758/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144746404","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":"Families' and clinicians' experiences with telehealth assessments for autism: A mixed-methods systematic review.","authors":"Panos Katakis, Paige Frankson, Georgia Lockwood Estrin, Jeanne Wolstencroft, Venus Mirzaei, Shermina Sayani, David Skuse, Michelle Heys","doi":"10.1371/journal.pdig.0000931","DOIUrl":"10.1371/journal.pdig.0000931","url":null,"abstract":"<p><p>Recently, the utilization of telehealth for the evaluation of autism spectrum disorder (ASD) in children has increased considerably. Although past studies have explored the feasibility and validity of telehealth assessment procedures for ASD, the acceptability and perspectives of families and clinicians regarding telehealth for autism evaluations have not yet been systematically examined. This mixed-methods systematic review aimed to synthesize the available evidence to understand the experiences of families and clinicians with telehealth. We followed the Joanna Briggs Institute methodology guidelines for conducting mixed-method systematic reviews using the convergent integrated approach. We searched relevant databases (EMBASE, MEDLINE, PsycINFO, CINAHL, ASSIA) and other sources (e.g., grey literature) to identify eligible articles (PROSPERO: CRD42022332500). Data from eligible studies were pooled and subjected to thematic synthesis. In total, 27 studies were included in this review, involving 1013 caregivers and 521 clinicians who shared their perceptions and experiences with telehealth. Overall, participants were highly satisfied with telehealth procedures and noted several advantages, including increased convenience, flexibility, and efficiency (e.g., reduced costs and travel time), improved service provision and access to timely care, and enhanced clinical effectiveness. However, certain disadvantages, such as technical difficulties, difficulties observing certain behaviors, perceived lack of accuracy, concerns about the family's role and safeguarding issues, among others, were also reported. Telehealth was believed to improve equity for some families (i.e., geographically remote families) while potentially disadvantaging others (i.e., socioeconomically disadvantaged families and those with limited English proficiency). Children who were older, less active, less medically and psychosocially complex and those with a clearer presentation of ASD were considered more suitable for a telehealth evaluation for ASD. In conclusion, this review provides new insights into the experiences of families and clinicians with telehealth, highlighting its potential uses for ASD evaluations and identifying areas for improvement and future research.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000931"},"PeriodicalIF":7.7,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12306760/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144746403","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":"Leveraging large language models for automated depression screening.","authors":"Bazen Gashaw Teferra, Argyrios Perivolaris, Wei-Ni Hsiang, Christian Kevin Sidharta, Alice Rueda, Karisa Parkington, Yuqi Wu, Achint Soni, Reza Samavi, Rakesh Jetly, Yanbo Zhang, Bo Cao, Sirisha Rambhatla, Sri Krishnan, Venkat Bhat","doi":"10.1371/journal.pdig.0000943","DOIUrl":"10.1371/journal.pdig.0000943","url":null,"abstract":"<p><p>Mental health diagnoses possess unique challenges that often lead to nuanced difficulties in managing an individual's well-being and daily functioning. Self-report questionnaires are a common practice in clinical settings to help mitigate the challenges involved in mental health disorder screening. However, these questionnaires rely on an individual's subjective response which can be influenced by various factors. Despite the advancements of Large Language Models (LLMs), quantifying self-reported experiences with natural language processing has resulted in imperfect accuracy. This project aims to demonstrate the effectiveness of zero-shot learning LLMs for screening and assessing item scales for depression using LLMs. The DAIC-WOZ is a publicly available mental health dataset that contains textual data from clinical interviews and self-report questionnaires with relevant mental health disorder labels. The RISEN prompt engineering framework was utilized to evaluate LLMs' effectiveness in predicting depression symptoms based on individual PHQ-8 items. Various LLMs, including GPT models, Llama3_8B, Cohere, and Gemini were assessed based on performance. The GPT models, especially GPT-4o, were consistently better than other LLMs (Llama3_8B, Cohere, Gemini) across all eight items of the PHQ-8 scale in accuracy (M = 75.9%), and F1 score (0.74). GPT models were able to predict PHQ-8 items related to emotional and cognitive states. Llama 3_8B demonstrated superior detection of anhedonia-related symptoms and the Cohere LLM's strength was identifying and predicting psychomotor activity symptoms. This study provides a novel outlook on the potential of LLMs for predicting self-reported questionnaire scores from textual interview data. The promising preliminary performance of the various models indicates there is potential that these models could effectively assist in the screening of depression. Further research is needed to establish a framework for which LLM can be used for specific mental health symptoms and other disorders. As well, analysis of additional datasets while fine-tuning models should be explored.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000943"},"PeriodicalIF":7.7,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12303271/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735934","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":"Integrating digital health technologies into the healthcare system: Challenges and opportunities in Nigeria.","authors":"Adaeze E Egwudo, Ayodapo O Jegede, Tolulope A Oyeniyi, Nkolika J Ezekwelu, Samirah N Abdu-Aguye, Azuka P Okwuraiwe, Chizaram A Onyeaghala, Theresa O Ozoude, Muritala O Suleiman, Grace O Aziken, Oluchukwu P Okeke, Olunike R Abodunrin, George U Eleje, Folahanmi T Akinsolu, Olajide O Sobande","doi":"10.1371/journal.pdig.0000928","DOIUrl":"10.1371/journal.pdig.0000928","url":null,"abstract":"<p><p>Integrating digital health technologies (DHTs) in Nigeria's healthcare system holds promise, yet the opportunities, challenges, and strategies influencing their success remain insufficiently explored. This scoping review aimed to map these factors, focusing on healthcare settings in Nigeria. A comprehensive search of databases (PubMed, Scopus, Web of Science, and CINAHL) and Google Scholar identified publications on DHT use in Nigeria from July 1, 2014, to June 30, 2024. A total of 31 observational and experimental studies were included involving healthcare workers, patients, caregivers, or other stakeholders impacted by DHT integration. Key findings revealed that DHTs enhanced treatment adherence, healthcare utilization, and community engagement while expanding technology infrastructure for scaling interventions. Notable opportunities included support and training and improved data quality. However, challenges such as operational and logistical barriers, inadequate network coverage, and cultural and gender sensitivity issues were prevalent. Strategies to address these challenges focused on continuous training for healthcare workers, community involvement to foster engagement, and data reporting and quality improvements. Despite their potential to transform healthcare delivery, particularly in underserved areas, successful integration of DHTs in Nigeria requires addressing infrastructure gaps, cultural norms, and operational challenges. Community engagement, capacity building for healthcare workers, and data-driven decision-making are critical to maximizing the impact of digital health interventions in Nigeria.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000928"},"PeriodicalIF":7.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12289021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710161","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 heroes among us: Leveraging Africa's youth for effective digital health implementation in communities.","authors":"Esther Opone","doi":"10.1371/journal.pdig.0000954","DOIUrl":"10.1371/journal.pdig.0000954","url":null,"abstract":"","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000954"},"PeriodicalIF":7.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12289079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710162","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":"Behavioral engagement patterns and psychosocial outcomes in web-based interpretation bias training for anxiety.","authors":"Jeremy William Eberle, Sonia Baee, Emma Catherine Wolfe, Mehdi Boukhechba, Daniel Harold Funk, Bethany Ann Teachman, Laura Elizabeth Barnes","doi":"10.1371/journal.pdig.0000945","DOIUrl":"10.1371/journal.pdig.0000945","url":null,"abstract":"<p><p>Digital mental health interventions (DMHIs) have the potential to expand treatment access for anxiety but often have low user engagement. The present study analyzed differences in psychosocial outcomes for different behavioral engagement patterns in a free web-based cognitive bias modification for interpretation (CBM-I) program. CBM-I is designed to shift interpretation biases common in anxiety by providing practice thinking about emotionally ambiguous situations in less threatening ways. Using data from 697 anxious community adults undergoing five weekly sessions of CBM-I in a clinical trial, we extracted program use markers based on task completion rate and time spent on training and assessment tasks. After using an exploratory cluster analysis of these markers to create two engagement groups (whose patterns ended up reflecting generally more vs. less time spent across tasks), we used multilevel models to test for group differences in interpretation bias and anxiety outcomes. Unexpectedly, engagement group did not significantly predict differential change in positive interpretation bias or anxiety. Further, participants who generally spent less time on the program (including both training and assessment tasks) improved in negative interpretation bias (on one of two measures) significantly more during the training phase than those who spent more time (and post hoc tests found were significantly older and slightly less educated). However, participants who generally spent less time had a significant loss in training gains for negative bias (on both measures) by 2-month follow-up. Findings highlight the challenge of interpreting time spent as a marker of engagement and the need to consider cognitive and affective markers of engagement in addition to behavioral markers. Further understanding engagement patterns holds promise for improving DMHIs for anxiety.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000945"},"PeriodicalIF":7.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12289040/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710160","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":"Video-based telemedicine utilization patterns and associated factors among racial and ethnic minorities in the United States during the COVID-19 pandemic: A mixed-methods scoping review.","authors":"John M Meddar, Ratnalekha V N Viswanadham, Defne L Levine, Tiffany R Martinez, Kendra Willis, Noah Choi, Jackson Douglas, Katharine S Lawrence","doi":"10.1371/journal.pdig.0000952","DOIUrl":"10.1371/journal.pdig.0000952","url":null,"abstract":"<p><p>The COVID-19 pandemic catalyzed a rapid expansion of telemedicine across the United States, expanding access to video-based services but also raising concerns about equitable access, use, and experience among minority populations. This mixed-methods scoping review quantitatively describes patterns of video-based telemedicine utilization and qualitatively evaluates factors impacting utilization among racial/ethnic minorities in the United States during the COVID-19 pandemic. We conducted a comprehensive literature search across six databases for studies published between January 2020 and March 2023. Eligible studies reported on telehealth or telemedicine use, specifically video-based visit utilization among racial/ethnic minorities. Reviewers independently screened studies, extracted data, and synthesized findings using an integrated mixed-methods approach. Of 1801 studies, 77 studies met the inclusion criteria. Of these, a majority were published in metropolitan coastal areas, and most were heterogeneous in their definition of telemedicine and utilization. Quantitatively, 33 studies (42.9%) reported increased use of video-based telemedicine, 29 (37.7%) reported decreased use, and 15 (20%) reported variable use across racial/ethnic subgroups. Most studies assessed disparities among non-Hispanic Black and Hispanic/Latinx populations (73 and 66 studies, respectively), while fewer examined disparities among other minority populations (45 studies). Factors associated with telemedicine adoption included patient- and community-level digital access barriers, low organizational digital capacity and infrastructure, implicit bias, and inadequate provider education and training. Identified facilitators included trust and awareness of telemedicine, adequate provider training, cultural and linguistic adaptations, targeted internet subsidies, and telemedicine reimbursements. Video-based telemedicine utilization among racial/ethnic minorities during the COVID-19 pandemic was heterogeneous, influenced by individual, systemic, and implementation factors. Disparities were most pronounced among Asians and other minority populations. Despite increased attention and efforts to address access barriers, our findings highlight the need for more targeted, culturally and structurally tailored interventions to improve digital inclusion.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000952"},"PeriodicalIF":7.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12289041/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710163","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":"Artificial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review.","authors":"Muhammad Ibtsaam Qadir, Jackson A Baril, Michele T Yip-Schneider, Duane Schonlau, Thi Thanh Thoa Tran, C Max Schmidt, Fiona R Kolbinger","doi":"10.1371/journal.pdig.0000920","DOIUrl":"10.1371/journal.pdig.0000920","url":null,"abstract":"<p><p>Based on the Fukuoka and Kyoto international consensus guidelines, the current clinical management of intraductal papillary mucinous neoplasm (IPMN) largely depends on imaging features. While these criteria are highly sensitive in detecting high-risk IPMN, they lack specificity, resulting in surgical overtreatment. Artificial Intelligence (AI)-based medical image analysis has the potential to augment the clinical management of IPMNs by improving diagnostic accuracy. Based on a systematic review of the academic literature on AI in IPMN imaging, 1041 publications were identified of which 25 published studies were included in the analysis. The studies were stratified based on prediction target, underlying data type and imaging modality, patient cohort size, and stage of clinical translation and were subsequently analyzed to identify trends and gaps in the field. Research on AI in IPMN imaging has been increasing in recent years. The majority of studies utilized CT imaging to train computational models. Most studies presented computational models developed on single-center datasets (n = 11,44%) and included less than 250 patients (n = 18,72%). Methodologically, convolutional neural network (CNN)-based algorithms were most commonly used. Thematically, most studies reported models augmenting differential diagnosis (n = 9,36%) or risk stratification (n = 10,40%) rather than IPMN detection (n = 5,20%) or IPMN segmentation (n = 2,8%). This systematic review provides a comprehensive overview of the research landscape of AI in IPMN imaging. Computational models have potential to enhance the accurate and precise stratification of patients with IPMN. Multicenter collaboration and datasets comprising various modalities are necessary to fully utilize this potential, alongside concerted efforts towards clinical translation.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000920"},"PeriodicalIF":7.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700628","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":"Machine learning in dentistry: a scoping review.","authors":"Shrey Lakhotia, Hormazd Godrej, Amandeep Kaur, Chaitanya Sai Nutakki, Michelle Mun, Pascal Eber, Leo Anthony Celi","doi":"10.1371/journal.pdig.0000940","DOIUrl":"10.1371/journal.pdig.0000940","url":null,"abstract":"<p><p>Artificial intelligence (AI), specifically machine learning (ML), is increasingly applied in decision-making for dental diagnosis, prognosis, and treatment. However, the methodological completeness of published models has not been rigorously assessed. We performed a scoping review of PubMed-indexed articles (English, 1 January 2018â€'31 December 2023) that used ML in any dental specialty. Each study was evaluated with the TRIPOD + AI rubric for key reporting elements such as data preprocessing, model validation, and clinical performance. Out of 1,506 identified studies, 280 met the inclusion criteria. Oral and maxillofacial radiology (27.5%), oral and maxillofacial surgery (15.0%), and general dentistry (14.3%) were the most represented specialties. Sixty-four studies (22.9%) lacked comparison with a clinical reference standard or existing model performing the same task. Most models focused on classification (59.6%), whereas generative applications were relatively rare (1.4%). Key gaps included limited assessment of model bias, poor outlier reporting, scarce calibration evaluation, low reproducibility, and restricted data access. ML could transform dental care, but robust calibration assessment and equity evaluation are critical for real-world adoption. Future research should prioritize error explainability, outlier reporting, reproducibility, fairness, and prospective validation.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000940"},"PeriodicalIF":7.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700629","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":"Using large language models to extract information from pediatric clinical reports.","authors":"Katharina Danhauser, Yingding Wang, Christoph Klein, Uta Tacke, Larissa Mantoan, Laura Aurica Ritter, Florian Heinen, Chiara Nobile, Moritz Tacke","doi":"10.1371/journal.pdig.0000919","DOIUrl":"10.1371/journal.pdig.0000919","url":null,"abstract":"<p><p>Most medical documentation, including clinical reports, exists in unstructured formats, which hinder efficient data analysis and integration into decision-making systems for patient care and research. Both fields could profit significantly from a reliable automatic analysis of these documents. Current methods for data extraction from these documents are labor-intensive and inflexible. Large Language Models (LLMs) offer a promising alternative for transforming unstructured medical documents into structured data in a flexible manner. This study assesses the performance of large language models (LLMs) in extracting structured data from pediatric clinical reports. Nine different LLMs were assessed. The results demonstrate that both commercial and open-source LLMs can achieve high accuracy in identifying patient-specific information, with top-performing models achieving over 90% accuracy in key tasks.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000919"},"PeriodicalIF":7.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700631","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}