Lancet Digital Health最新文献

{"title":"AI for medical diagnosis: does a single negative trial mean it is ineffective?","authors":"Olga Kostopoulou , Brendan Delaney","doi":"10.1016/j.landig.2025.01.005","DOIUrl":"10.1016/j.landig.2025.01.005","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"Pages e108-e109"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of emergency admissions: trade-offs between model simplicity and performance","authors":"Shishir Rao , Kazem Rahimi","doi":"10.1016/j.landig.2024.12.008","DOIUrl":"10.1016/j.landig.2024.12.008","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"Pages e106-e107"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From the 100 Day Mission to 100 lines of software development: how to improve early outbreak analytics","authors":"Carmen Tamayo Cuartero DVM PhD ,&nbsp;Anna C Carnegie MPP ,&nbsp;Zulma M Cucunuba MD PhD ,&nbsp;Anne Cori PhD ,&nbsp;Sara M Hollis MSc ,&nbsp;Rolina D Van Gaalen PhD ,&nbsp;Amrish Y Baidjoe ,&nbsp;Alexander F Spina MPH ,&nbsp;John A Lees PhD ,&nbsp;Simon Cauchemez PhD ,&nbsp;Mauricio Santos PhD ,&nbsp;Juan D Umaña MSc ,&nbsp;Chaoran Chen PhD ,&nbsp;Hugo Gruson PhD ,&nbsp;Pratik Gupte PhD ,&nbsp;Joseph Tsui MSc ,&nbsp;Anita A Shah MPH ,&nbsp;Geraldine Gomez Millan SEP ,&nbsp;David Santiago Quevedo MSc ,&nbsp;Neale Batra MSc ,&nbsp;Prof Adam J Kucharski","doi":"10.1016/S2589-7500(24)00218-8","DOIUrl":"10.1016/S2589-7500(24)00218-8","url":null,"abstract":"<div><div>Since the COVID-19 pandemic, considerable advances have been made to improve epidemic preparedness by accelerating diagnostics, therapeutics, and vaccine development. However, we argue that it is crucial to make equivalent efforts in the field of outbreak analytics to help ensure reliable, evidence-based decision making. To explore the challenges and key priorities in the field of outbreak analytics, the Epiverse-TRACE initiative brought together a multidisciplinary group of experts, including field epidemiologists, data scientists, academics, and software engineers from public health institutions across multiple countries. During a 3-day workshop, 40 participants discussed what the first 100 lines of code written during an outbreak should look like. The main findings from this workshop are summarised in this Viewpoint. We provide an overview of the current outbreak analytic landscape by highlighting current key challenges that should be addressed to improve the response to future public health crises. Furthermore, we propose actionable solutions to these challenges that are achievable in the short term, and longer-term strategic recommendations. This Viewpoint constitutes a call to action for experts involved in epidemic response to develop modern and robust data analytic approaches at the heart of epidemic preparedness and response.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"Pages e161-e166"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnoses supported by a computerised diagnostic decision support system versus conventional diagnoses in emergency patients (DDX-BRO): a multicentre, multiple-period, double-blind, cluster-randomised, crossover superiority trial","authors":"Wolf E Hautz MD ,&nbsp;Thimo Marcin PhD ,&nbsp;Stefanie C Hautz PhD ,&nbsp;Stefan K Schauber PhD ,&nbsp;Prof Gert Krummrey MD ,&nbsp;Martin Müller MD ,&nbsp;Thomas C Sauter MD ,&nbsp;Cornelia Lambrigger RN ,&nbsp;David Schwappach PhD ,&nbsp;Prof Mathieu Nendaz MD ,&nbsp;Gregor Lindner MD ,&nbsp;Simon Bosbach MD ,&nbsp;Ines Griesshammer MD ,&nbsp;Philipp Schönberg MD ,&nbsp;Emanuel Plüss MD ,&nbsp;Valerie Romann MD ,&nbsp;Svenja Ravioli MD ,&nbsp;Nadine Werthmüller MD ,&nbsp;Fabian Kölbener MD ,&nbsp;Prof Aristomenis K Exadaktylos MD ,&nbsp;Laura Zwaan PhD","doi":"10.1016/S2589-7500(24)00250-4","DOIUrl":"10.1016/S2589-7500(24)00250-4","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Diagnostic error is a frequent and clinically relevant health-care problem. Whether computerised diagnostic decision support systems (CDDSSs) improve diagnoses is controversial, and prospective randomised trials investigating their effectiveness in routine clinical practice are scarce. We hypothesised that diagnoses made with a CDDSS in the emergency department setting would be superior to unsupported diagnoses.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;This multicentre, multiple-period, double-blind, cluster-randomised, crossover superiority trial was done in four emergency departments in Switzerland. Eligible patients were adults (aged ≥18 years) presenting with abdominal pain, fever of unknown origin, syncope, or non-specific symptoms. Emergency departments were randomly assigned (1:1) to one of two predefined sequences of six alternating periods of intervention or control. Patients presenting during an intervention period were diagnosed with the aid of a CDDSS, whereas patients presenting during a control period were diagnosed without a CDDSS (usual care). Patients and personnel assessing outcomes were masked to group allocation; treating physicians were not. The primary binary outcome (false or true) was a composite score indicating a risk of reduced diagnostic quality, which was deemed to be present if any of the following occurred within 14 days: unscheduled medical care, a change in diagnosis, an unexpected intensive care unit admission within 24 h if initially admitted to hospital, or death. We assessed superiority of supported versus unsupported diagnoses in all consenting patients using a generalised linear mixed effects model. All participants who received any study treatment (including control) and completed the study were included in the safety analysis. This trial is registered with &lt;span&gt;&lt;span&gt;ClinicalTrials.gov&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt; (&lt;span&gt;&lt;span&gt;NCT05346523&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;) and is closed to accrual.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;Between June 9, 2022, and June 23, 2023, 15 845 patients were screened and 1204 (591 [49·1%] female and 613 [50·9%] male) were included in the primary efficacy analysis. The median age of participants was 53 years (IQR 34–69). Diagnostic quality risk was observed in 100 (18%) of 559 patients with CDDSS-supported diagnoses and 119 (18%) of 645 with unsupported diagnoses (adjusted odds ratio 0·96 [95% CI 0·71–1·3]). 94 (7·8%) patients suffered a serious adverse event, none related to the study.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Interpretation&lt;/h3&gt;&lt;div&gt;Use of a CDDSS did not reduce the occurrence of diagnostic quality risk compared with the usual diagnostic process in adults presenting to emergency departments. Future research should aim to identify specific contexts in which CDDSSs are effective and how existing CDDSSs can be adapted to improve patient outcomes.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Funding&lt;/h3&gt;&lt;div&gt;Swiss National Science Foundation and University Hospi","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"Pages e136-e144"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thank you to The Lancet Digital Health's statistical and peer reviewers in 2024","authors":"The Lancet Digital Health Editors","doi":"10.1016/j.landig.2025.01.009","DOIUrl":"10.1016/j.landig.2025.01.009","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"Pages e110-e112"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using artificial intelligence to switch from accident to sagacity in the serendipitous detection of uncommon diseases","authors":"Roberto Sciagrà","doi":"10.1016/j.landig.2024.12.006","DOIUrl":"10.1016/j.landig.2024.12.006","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"Pages e104-e105"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing temporal patterns in administrative patient data to predict risk of emergency hospital admission","authors":"Benjamin Post MBChB ,&nbsp;Roman Klapaukh PhD ,&nbsp;Prof Stephen J Brett MD ,&nbsp;Prof A Aldo Faisal PhD","doi":"10.1016/S2589-7500(24)00254-1","DOIUrl":"10.1016/S2589-7500(24)00254-1","url":null,"abstract":"<div><h3>Background</h3><div>Unplanned hospital admissions are associated with worse patient outcomes and cause strain on health systems worldwide. Primary care electronic health records (EHRs) have successfully been used to create prediction models for emergency hospitalisation, but these approaches require a broad range of diagnostic, physiological, and laboratory values. In this study, we aimed to capture temporal patterns of patient activity from EHR data and evaluate their effectiveness in predicting emergency hospital admissions compared with conventional methods.</div></div><div><h3>Methods</h3><div>In this retrospective observational study, we used the Secure Anonymised Information Linkage databank to extract temporal patterns of primary care activity from undifferentiated electronic health record timestamp data for 1·37 million patients in Wales aged 18–80 years with at least one recorded Read code between the years 2016 and 2018. Using Gaussian mixture modelling we grouped patients into distinct temporal clusters, performed a three-stage validation of our approach and calculated the risk of emergency hospital admission for each temporal cluster group. Finally, these temporal clusters were combined with five administrative variables and incorporated into four emergency hospital admission prediction models (logistic regression, naive Bayes, XGBoost, and multilayer perceptron [MLP]) and compared with a more traditional, but data-intensive, modelling technique. The primary outcome was emergency hospital admission as the next health-care event.</div></div><div><h3>Findings</h3><div>Six distinct temporal cluster patterns of primary care EHR activity were identified, associated with varying risks of future emergency hospital admission risk. These patterns were visually interpretable, repeatable at a population-level, and clinically plausible. The best emergency hospital admission prediction model (MLP) achieved an area under the receiver operating characteristic (AUROC) of 0·82 and precision of 0·94 in regional cohorts. In external validation in regional cohorts, similar model performance was observed (AUROC 0·82 and precision 0·92). This model also matched the performance of a more complex model (extended feature model) requiring 33 clinical parameters (AUROC 0·82 <em>vs</em> 0·83; precision 0·94 <em>vs</em> 0·90) for the same task on the same dataset.</div></div><div><h3>Interpretation</h3><div>We developed a novel machine learning pipeline that extracts interpretable temporal patterns from simple representations of EHR data and can be incorporated into emergency hospital admission predictors. This framework might enable more rapid development of parsimonious clinical prediction models.</div></div><div><h3>Funding</h3><div>UKRI CDT in AI for Healthcare, UKRI Turing AI Fellowship, NIHR Imperial Biomedical Research Centre, and Research Capability Funding.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"Pages e124-e135"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prevalence and demographics of 331 rare diseases and associated COVID-19-related mortality among 58 million individuals: a nationwide retrospective observational study","authors":"Johan H Thygesen PhD ,&nbsp;Huayu Zhang PhD ,&nbsp;Hanane Issa MSc ,&nbsp;Jinge Wu MSc ,&nbsp;Tuankasfee Hama MSc ,&nbsp;Ana-Caterina Phiho-Gomes PhD ,&nbsp;Tudor Groza PhD ,&nbsp;Sara Khalid PhD ,&nbsp;Thomas R Lumbers PhD ,&nbsp;Mevhibe Hocaoglu PhD ,&nbsp;Prof Kamlesh Khunti PhD ,&nbsp;Rouven Priedon BA ,&nbsp;Prof Amitava Banerjee PhD ,&nbsp;Nikolas Pontikos PhD ,&nbsp;Chris Tomlinson PhD ,&nbsp;Ana Torralbo PhD ,&nbsp;Prof Paul Taylor PhD ,&nbsp;Prof Cathie Sudlow PhD ,&nbsp;Prof Spiros Denaxas PhD ,&nbsp;Prof Harry Hemingway PhD ,&nbsp;Prof Honghan Wu PhD","doi":"10.1016/S2589-7500(24)00253-X","DOIUrl":"10.1016/S2589-7500(24)00253-X","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;The Global Burden of Disease Study has provided key evidence to inform clinicians, researchers, and policy makers across common diseases, but no similar effort with a single-study design exists for hundreds of rare diseases. Consequently, for many rare conditions there is little population-level evidence, including prevalence and clinical vulnerability, resulting in an absence of evidence-based care that was prominent during the COVID-19 pandemic. We aimed to inform rare disease care by providing key descriptors from national data and explore the impact of rare diseases during the COVID-19 pandemic.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;In this nationwide retrospective observational cohort study, we used the electronic health records (EHRs) of more than 58 million people in England, linking nine National Health Service datasets spanning health-care settings for people who were alive on Jan 23, 2020. Starting with all rare diseases listed in Orphanet (an extensive online resource for rare diseases), we quality assured and filtered down to analyse 331 conditions mapped to ICD-10 or Systemized Nomenclature of Medicine–Clinical Terms that were clinically validated in our dataset. For all 331 rare diseases, we calculated population prevalences, analysed patients’ clinical and demographic details, and investigated mortality with SARS-CoV-2. We assessed COVID-19-related mortality by comparing cohorts of patients for each rare disease and rare disease category with controls matched for age group, sex, ethnicity, and vaccination status, at a ratio of two controls per individual with a rare disease.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;Of 58 162 316 individuals, we identified 894 396 with at least one rare disease and assessed COVID-19-related mortality between Sept 1, 2020, and Nov 30, 2021. We calculated reproducible estimates, adjusted for age and sex, for all 331 rare diseases, including for 186 (56·2%) conditions without existing prevalence estimates in Orphanet. 49 rare diseases were significantly more frequent in female individuals than in male individuals, and 62 were significantly more frequent in male individuals than in female individuals; 47 were significantly more frequent in Asian or British Asian individuals than in White individuals; and 22 were significantly more frequent in Black or Black British individuals than in White individuals. 37 rare diseases were significantly more frequent in the White population compared with either the Black or Asian population. 7965 (0·9%) of 894 396 patients with a rare disease died from COVID-19, compared with 141 287 (0·2%) of 58 162 316 in the full study population. In fully vaccinated individuals, the risk of COVID-19-related mortality was significantly higher for eight rare diseases, with patients with bullous pemphigoid (hazard ratio 8·07, 95% CI 3·01–21·62) being at highest risk.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Interpretation&lt;/h3&gt;&lt;div&gt;Our study highlights that national-scale EHRs pro","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 2","pages":"Pages e145-e156"},"PeriodicalIF":23.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical trials for implantable neural prostheses: understanding the ethical and technical requirements","authors":"Marcello Ienca PhD ,&nbsp;Giacomo Valle PhD ,&nbsp;Prof Stanisa Raspopovic PhD","doi":"10.1016/S2589-7500(24)00222-X","DOIUrl":"10.1016/S2589-7500(24)00222-X","url":null,"abstract":"<div><div>Neuroprosthetics research has entered a stage in which animal models and proof-of-concept studies are translated into clinical applications, often combining implants with artificial intelligence techniques. This new phase raises the question of how clinical trials should be designed to scientifically and ethically address the unique features of neural prostheses. Neural prostheses are complex cyberbiological devices able to acquire and process data; hence, their assessment is not reducible to only third-party safety and efficacy evaluations as in pharmacological research. In addition, assessment of neural prostheses requires a causal understanding of their mechanisms, and scrutiny of their information security and legal liability standards. Some neural prostheses affect not only human behaviour, but also psychological faculties such as consciousness, cognition, and affective states. In this Viewpoint, we argue that the technological novelty of neural prostheses could generate challenges for technology assessment, clinical validation, and research ethics oversight. To this end, we identify a set of methodological and research ethics challenges specific to this medical technology innovation. We provide insights into relevant ethical guidelines and assess whether oversight mechanisms are well equipped to ensure adequate clinical and ethical use. Finally, we outline patient-centred research ethics requirements for clinical trials involving implantable neural prostheses.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 3","pages":"Pages e216-e224"},"PeriodicalIF":23.8,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large language models for the mental health community: framework for translating code to care","authors":"Matteo Malgaroli PhD ,&nbsp;Katharina Schultebraucks PhD ,&nbsp;Keris Jan Myrick MS ,&nbsp;Alexandre Andrade Loch MD ,&nbsp;Laura Ospina-Pinillos PhD ,&nbsp;Tanzeem Choudhury PhD ,&nbsp;Roman Kotov PhD ,&nbsp;Munmun De Choudhury PhD ,&nbsp;John Torous MD","doi":"10.1016/S2589-7500(24)00255-3","DOIUrl":"10.1016/S2589-7500(24)00255-3","url":null,"abstract":"<div><div>Large language models (LLMs) offer promising applications in mental health care to address gaps in treatment and research. By leveraging clinical notes and transcripts as data, LLMs could improve diagnostics, monitoring, prevention, and treatment of mental health conditions. However, several challenges persist, including technical costs, literacy gaps, risk of biases, and inequalities in data representation. In this Viewpoint, we propose a sociocultural–technical approach to address these challenges. We highlight five key areas for development: (1) building a global clinical repository to support LLMs training and testing, (2) designing ethical usage settings, (3) refining diagnostic categories, (4) integrating cultural considerations during development and deployment, and (5) promoting digital inclusivity to ensure equitable access. We emphasise the need for developing representative datasets, interpretable clinical decision support systems, and new roles such as digital navigators. Only through collaborative efforts across all stakeholders, unified by a sociocultural–technical framework, can we clinically deploy LLMs while ensuring equitable access and mitigating risks.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Pages e282-e285"},"PeriodicalIF":23.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142956984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}