PLOS digital health最新文献

{"title":"An inherently interpretable AI model improves screening speed and accuracy for early diabetic retinopathy.","authors":"Kerol Djoumessi, Ziwei Huang, Laura Kühlewein, Annekatrin Rickmann, Natalia Simon, Lisa M Koch, Philipp Berens","doi":"10.1371/journal.pdig.0000831","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000831","url":null,"abstract":"<p><p>Diabetic retinopathy (DR) is a frequent complication of diabetes, affecting millions worldwide. Screening for this disease based on fundus images has been one of the first successful use cases for modern artificial intelligence in medicine. However, current state-of-the-art systems typically use black-box models to make referral decisions, requiring post-hoc methods for AI-human interaction and clinical decision support. We developed and evaluated an inherently interpretable deep learning model, which explicitly models the local evidence of DR as part of its network architecture, for clinical decision support in early DR screening. We trained the network on 34,350 high-quality fundus images from a publicly available dataset and validated its performance on a large range of ten external datasets. The inherently interpretable model was compared to post-hoc explainability techniques applied to a standard DNN architecture. For comparison, we obtained detailed lesion annotations from ophthalmologists on 65 images to study if the class evidence maps highlight clinically relevant information. We tested the clinical usefulness of our model in a retrospective reader study, where we compared screening for DR without AI support to screening with AI support with and without AI explanations. The inherently interpretable deep learning model obtained an accuracy of .906 [.900-.913] (95%-confidence interval) and an AUC of .904 [.894-.913] on the internal test set and similar performance on external datasets, comparable to the standard DNN. High evidence regions directly extracted from the model contained clinically relevant lesions such as microaneurysms or hemorrhages with a high precision of .960 [.941-.976], surpassing post-hoc techniques applied to a standard DNN. Decision support by the model highlighting high-evidence regions in the image improved screening accuracy for difficult decisions and improved screening speed. This shows that inherently interpretable deep learning models can provide clinical decision support while obtaining state-of-the-art performance improving human-AI collaboration.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000831"},"PeriodicalIF":0.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12068651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144063482","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":"Practitioners' perspectives on implementation of acute virtual wards: A scoping review.","authors":"Theresa Sunny, Nandakumar Ravichandran, John Broughan, Geoff McCombe, Sheila Loughman, Kenneth McDonald, Neasa Starr, Walter Cullen","doi":"10.1371/journal.pdig.0000858","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000858","url":null,"abstract":"<p><p>Virtual wards provide a promising alternative to traditional 'bedded care' by facilitating early discharges and delivering acute care at home. They focus specifically on patients needing acute care, which would traditionally necessitate an in-hospital stay. Understanding practitioners' beliefs and attitudes is crucial for successful implementation and operation of Virtual wards. This scoping review explores practitioners' perspectives on the implementation of virtual wards. A total of 18 studies were included in the final analysis from the 201 studies identified initially through searches in PubMed, Cochrane, CINAHL, and Embase databases (2015-2024) following PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. Thematic analysis was conducted using Braun and Clarke's framework to identify key insights. Thematic analysis revealed key themes related to implementation, quality of care, technology, training, and awareness. These themes highlight the challenges influencing the adoption and considerations for the operational success of virtual wards. Virtual wards demonstrate significant potential for delivering acute care efficiently and sustainably. However, challenges related to service design, patient safety, technology integration, and workforce training must be addressed to ensure their successful implementation and long-term efficacy.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000858"},"PeriodicalIF":0.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12063850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057899","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":"Wearables research for continuous monitoring of patient outcomes: A scoping review.","authors":"Kalee Lodewyk, Madeleine Wiebe, Liz Dennett, Jake Larsson, Andrew Greenshaw, Jake Hayward","doi":"10.1371/journal.pdig.0000860","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000860","url":null,"abstract":"<p><strong>Background: </strong>The use of wearable devices for remote health monitoring is a rapidly expanding field. These devices might benefit patients and providers; however, they are not yet widely used in healthcare. This scoping review assesses the current state of the literature on wearable devices for remote health monitoring in non-hospital settings.</p><p><strong>Methods: </strong>CINAHL, Scopus, Embase and MEDLINE were searched until August 5, 2024. We performed citation searching and searched Google Scholar. Studies on wearable devices in an outpatient setting with a clinically relevant, measurable outcome were included and were categorized according to intended use of data: monitoring of existing disease vs. diagnosis of new disease.</p><p><strong>Results: </strong>Eighty studies met eligibility criteria. Most studies used device data to monitor a chronic disease (68/80, 85%), most often neurodegenerative (22/68, 32%). Twelve studies (12/80, 15%) used device data to diagnose new disease, majority being cardiovascular (9/12, 75%). A range of wearable devices were studied with watches and bracelets being most common (50/80, 63%). Only six studies (8%) were randomized controlled trials, four of which (67%) showed evidence of positive clinical impact. Feasibility determinants were inconsistently reported, including compliance (51/80, 64%), patient-reported useability (13/80, 16%), and participant technology literacy (1/80, 1%).</p><p><strong>Conclusions: </strong>Evidence for clinical effectiveness of wearable devices remains scant. Heterogeneity across studies in terms of devices, disease targets and monitoring protocols makes data synthesis challenging, especially given the rapid pace of technical innovation. These findings provide direction for future research and implementation of wearable devices in healthcare.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000860"},"PeriodicalIF":0.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12063813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061544","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":"Clinical insights: A comprehensive review of language models in medicine.","authors":"Nikita Neveditsin, Pawan Lingras, Vijay Mago","doi":"10.1371/journal.pdig.0000800","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000800","url":null,"abstract":"<p><p>This paper explores the advancements and applications of language models in healthcare, focusing on their clinical use cases. It examines the evolution from early encoder-based systems requiring extensive fine-tuning to state-of-the-art large language and multimodal models capable of integrating text and visual data through in-context learning. The analysis emphasizes locally deployable models, which enhance data privacy and operational autonomy, and their applications in tasks such as text generation, classification, information extraction, and conversational systems. The paper also highlights a structured organization of tasks and a tiered ethical approach, providing a valuable resource for researchers and practitioners, while discussing key challenges related to ethics, evaluation, and implementation.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000800"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12061104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065355","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":"Feasibility of HABIT-ILE@home in children with cerebral palsy and adults with chronic stroke: A pilot study.","authors":"Edouard Ducoffre, Carlyne Arnould, Merlin Somville, Zélie Rosselli, Geoffroy Saussez, Yannick Bleyenheuft","doi":"10.1371/journal.pdig.0000850","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000850","url":null,"abstract":"<p><strong>Introduction: </strong>Children with cerebral palsy (CP) and adults with chronic stroke (CS) usually have disabilities in voluntary motor control. Hand-Arm Bimanual Intensive Therapy Including Lower Extremities (HABIT-ILE), an evidence-based therapy, has always been provided during day camps. This pilot study investigates if HABIT-ILE@home, a remote neurorehabilitation, is feasible for children with CP and adults with CS.</p><p><strong>Methods: </strong>Four children with CP (5-18y) and three adults with CS were recruited. They received 15h (5x3h) of HABIT-ILE@home provided by a caregiver with a remote supervision of 30min at the beginning and end of each session. A large touch screen, the REAtouch Lite, was used as a support for the therapy. An interview based on a questionnaire (n = 73 items for CP/ n = 74 items for stroke patients; scored from 0 \"disagree\" to 3 \"agree\", a higher rating meaning a more positive aspect of the therapy) was conducted with patients and their caregivers after 15h of supervised home-therapy to assess their adherence to the treatment and the feasibility of HABIT-ILE@home. Performance and satisfaction in achieving functional goals were assessed before and after the intervention using the Canadian Occupational Performance Measure (COPM).</p><p><strong>Results: </strong>Caregivers felt sufficiently supported by the supervision team (medians = 3) to carry out HABIT-ILE@home sessions thanks to an adequate clinical supervision (CP median = 2.6; CS median = 2.9). HABIT-ILE principles were transferable at patients' home (CP median = 2.6; CS median = 2.8). The impact of the therapy on daily organization was more problematic for children's caregivers (median = 1.5) than for adults' caregivers (median = 3). Children with CP enjoyed the therapy (median = 2) but felt that it was too long (median = 1) and significant fatigue was present (median = 1.3). CS adults did not find the therapy fun (median = 1) but considered it as extremely useful (median = 3). Although the motivational source differed between children and adults, this did not seem to strongly affect adherence to treatment. Performance and satisfaction in achieving functional goals improved over the MCID (2 points) for all CS participants and for 3 out 4 CP children.</p><p><strong>Conclusion: </strong>HABIT-ILE@home seems to be feasible for children with CP and adults with CS. It may allow more patients to benefit from an efficient neurorehabilitation, whatever sanitary conditions or patients' home geographical locations.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000850"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12061106/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144025986","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":"Exploring the complex nature of implementation of Artificial intelligence in clinical practice: an interview study with healthcare professionals, researchers and Policy and Governance Experts.","authors":"Jobbe P L Leenen, Paul Hiemstra, Martine M Ten Hoeve, Anouk C J Jansen, Joris D van Dijk, Brian Vendel, Guido Versteeg, Gido A Hakvoort, Marike Hettinga","doi":"10.1371/journal.pdig.0000847","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000847","url":null,"abstract":"<p><p>Artificial Intelligence (AI)-based tools have shown potential to optimize clinical workflows, enhance patient quality and safety, and facilitate personalized treatment. However, transitioning viable AI solutions to clinical implementation remains limited. To understand the challenges of bringing AI into clinical practice, we explored the experiences of healthcare professionals, researchers, and Policy and Governance Experts in hospitals. We conducted a qualitative study with thirteen semi-structured interviews (mean duration 52.1 ± 5.4 minutes) with healthcare professionals, researchers, and Policy and Governance Experts, with prior experience on AI development in hospitals. The interview guide was based on value, application, technology, governance, and ethics from the Innovation Funnel for Valuable AI in Healthcare, and the discussions were analyzed through thematic analysis. Six themes emerged: (1) demand-pull vs. tech-push: AI development focusing on innovative technologies may face limited success in large-scale clinical implementation. (2) Focus on generating knowledge, not solutions: Current AI initiatives often generate knowledge without a clear path for implementing AI models once proof-of-concept is achieved. (3) Lack of multidisciplinary collaboration: Successful AI initiatives require diverse stakeholder involvement, often hindered by late involvement and challenging communication. (4) Lack of appropriate skills: Stakeholders, including IT departments and healthcare professionals, often lack the required skills and knowledge for effective AI integration in clinical workflows. (5) The role of the hospital: Hospitals need a clear vision for integrating AI, including meeting preconditions in infrastructure and expertise. (6) Evolving laws and regulations: New regulations can hinder AI development due to unclear implications but also enforce standardization, emphasizing quality and safety in healthcare. In conclusion, this study highlights the complexity of AI implementation in clinical settings. Multidisciplinary collaboration is essential and requires facilitation. Balancing divergent perspectives is crucial for successful AI implementation. Hospitals need to assess their readiness for AI, develop clear strategies, standardize development processes, and foster better collaboration among stakeholders.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000847"},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12057897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045872","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":"Empowering Ethiopia's digital citizenship in early-career healthcare leadership.","authors":"Sharmi Haque","doi":"10.1371/journal.pdig.0000845","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000845","url":null,"abstract":"","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000845"},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12057954/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144025985","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":"Comparing the predictive discrimination of machine learning models for ordinal outcomes: A case study of dehydration prediction in patients with acute diarrhea.","authors":"Kexin Qu, Monique Gainey, Samika S Kanekar, Sabiha Nasrim, Eric J Nelson, Stephanie C Garbern, Mahmuda Monjory, Nur H Alam, Adam C Levine, Christopher H Schmid","doi":"10.1371/journal.pdig.0000820","DOIUrl":"10.1371/journal.pdig.0000820","url":null,"abstract":"<p><p>Many comparisons of statistical regression and machine learning algorithms to build clinical predictive models use inadequate methods to build regression models and do not have proper independent test sets on which to externally validate the models. Proper comparisons for models of ordinal categorical outcomes do not exist. We set out to compare model discrimination for four regression and machine learning methods in a case study predicting the ordinal outcome of severe, some, or no dehydration among patients with acute diarrhea presenting to a large medical center in Bangladesh using data from the NIRUDAK study derivation and validation cohorts. Proportional Odds Logistic Regression (POLR), penalized ordinal regression (RIDGE), classification trees (CART), and random forest (RF) models were built to predict dehydration severity and compared using three ordinal discrimination indices: ordinal c-index (ORC), generalized c-index (GC), and average dichotomous c-index (ADC). Performance was evaluated on models developed on the training data, on the same models applied to an external test set and through internal validation with three bootstrap algorithms to correct for overoptimism. RF had superior discrimination on the original training data set, but its performance was more similar to the other three methods after internal validation using the bootstrap. Performance for all models was lower on the prospective test dataset, with particularly large reduction for RF and RIDGE. POLR had the best performance in the test dataset and was also most efficient, with the smallest final model size. Clinical prediction models for ordinal outcomes, just like those for binary and continuous outcomes, need to be prospectively validated on external test sets if possible because internal validation may give a too optimistic picture of model performance. Regression methods can perform as well as more automated machine learning methods if constructed with attention to potential nonlinear associations. Because regression models are often more interpretable clinically, their use should be encouraged.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000820"},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12054866/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065365","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":"AI-driven personalized nutrition: RAG-based digital health solution for obesity and type 2 diabetes.","authors":"Anand K Gavai, Jos van Hillegersberg","doi":"10.1371/journal.pdig.0000758","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000758","url":null,"abstract":"<p><p>Effective management of obesity and type 2 diabetes is a major global public health challenge that requires evidence-based, scalable personalized nutrition solutions. Here, we present an artificial intelligence (AI) driven dietary recommendation system that generates personalized smoothie recipes while prioritizing health outcomes and environmental sustainability. A key feature of the system is the \"virtual nutritionist\", an iterative validation framework that dynamically refines recipes to meet predefined nutritional and sustainability criteria. The system integrates dietary guidelines from the National Institute for Public Health and the Environment (RIVM), EUFIC, USDA FoodData Central, and the American Diabetes Association with retrieval-augmented generation (RAG) to deliver evidence-based recommendations. By aligning with the United Nations Sustainable Development Goals (SDGs), the system promotes plant-based, seasonal, and locally sourced ingredients to reduce environmental impact. We leverage explainable AI (XAI) to enhance user engagement through clear explanations of ingredient benefits and interactive features, improving comprehension across varying health literacy levels. Using zero-shot and few-shot learning techniques, the system adapts to user inputs while maintaining privacy through local deployment of the LLaMA3 model. In evaluating 1,000 recipes, the system achieved 80.1% adherence to health guidelines meeting targets for calories, fiber, and fats and 92% compliance with sustainability criteria, emphasizing seasonal and locally sourced ingredients. A prototype web application enables real-time, personalized recommendations, bridging the gap between AI-driven insights and clinical dietary management. This research underscores the potential of AI-driven precision nutrition to revolutionize chronic disease management by improving dietary adherence, enhancing health literacy, and offering a scalable, adaptable solution for clinical workflows, telehealth platforms, and public health initiatives, with the potential to significantly alleviate the global healthcare burden.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000758"},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12054865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060111","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 citizen science for ethical monitoring of youth physical activity frequency: Comparing mobile ecological prospective assessments and retrospective recall.","authors":"Sheriff Tolulope Ibrahim, Jamin Patel, Tarun Reddy Katapally","doi":"10.1371/journal.pdig.0000840","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000840","url":null,"abstract":"<p><p>Physical inactivity is a leading risk factor for mortality worldwide. Understanding youth patterns of moderate-to-vigorous physical activity (MVPA) is essential for addressing non-communicable diseases. Digital citizen science approaches, using citizen-owned smartphones for data collection, offer an ethical and innovative method for monitoring MVPA. This study compares the frequency of MVPA reported by youth using retrospective surveys and mobile ecological prospective momentary assessments (mEPAs) to explore the potential of digital citizen science for physical activity (PA) surveillance. Youth (N = 808) were recruited from Saskatchewan, Canada, between August and December 2018. Sixty-eight participants (ages 13-21) provided complete data on retrospective surveys (International Physical Activity Questionnaire, Simple Physical Activity Questionnaire, Global Physical Activity Questionnaire) and prospective mEPAs. Wilcoxon signed-rank tests compared retrospective and prospective MVPA frequencies, while negative binomial regression analysis examined associations between contextual factors and MVPA. Significant differences were found in the frequency of MVPA reported via retrospective surveys versus mEPAs (p < 0.000). Prospective MVPA was associated with family and friend support, having drug-free friends, part-time employment, and school distance, while retrospective MVPA frequency was associated with school and strength training. Digital citizen science, utilizing mEPAs, can provide more accurate and timely data on youth MVPA. With increasing smartphone access and digital literacy, mEPAs represent a promising method for developing effective and personalized MVPA recommendations for youth. However, these findings should be interpreted with caution, as the sample represents a small subset of youth, limiting generalizability to other youth populations.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 5","pages":"e0000840"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12047779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014682","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}