Diagnosis最新文献

{"title":"Can AI assist in reducing diagnostic error? A narrative review.","authors":"Ian A Scott","doi":"10.1515/dx-2026-0021","DOIUrl":"https://doi.org/10.1515/dx-2026-0021","url":null,"abstract":"<p><p>Diagnostic error, defined as missed, wrong, or delayed diagnoses or those not communicated to patients, is common, affecting 5-10 % of hospital admissions and clinic visits. Such errors cause patient harm in up to 1 in 100 of such encounters and account for 10 % of all hospital deaths and serious adverse events. About 80 % of diagnostic errors are potentially preventable, most resulting from flaws in clinician reasoning in formulating and testing diagnostic hypotheses. The advent of artificial intelligence (AI), and large language models (LLMs) in particular, has attracted great interest in how these technologies can reduce diagnostic error within the context of bedside or clinic consultations. This narrative review aims to provide practising clinicians with a comprehensible analysis of where AI and LLMs are currently positioned in assisting diagnostic performance in clinician-patient encounters based on contemporary state-of-the-art research. It attempts to answer seven questions relevant to clinician understanding and adoption of AI/LLMs. It concludes that AI tools have matured to the extent that they can improve diagnostic decision-making of clinicians and can assist institutions in increasing diagnostic safety. The rapid development of LLMs and ongoing release of new versions necessitate continuous monitoring of their evolving diagnostic capabilities. Importantly, a balanced approach is required where LLMs work to complement, rather than replace, the nuanced diagnostic reasoning of clinicians.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148535083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mast cell activation syndrome (MCAS) by another name? Homage to the Historical Precedent of Consensus 2 MCAS.","authors":"Harriet A Carroll","doi":"10.1515/dx-2026-0110","DOIUrl":"10.1515/dx-2026-0110","url":null,"abstract":"","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148434926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The patient narrative distortion index (PNDI): a framework to detect narrative loss as a hidden driver of diagnostic error.","authors":"Yogesh Kumar Gupta","doi":"10.1515/dx-2026-0086","DOIUrl":"https://doi.org/10.1515/dx-2026-0086","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Patient narratives are foundational to diagnosis, yet clinicians frequently and unintentionally distort, minimize, or reinterpret these narratives during clinical encounters. These distortions - termed patient narrative distortion - are unmeasured contributors to diagnostic error [G.D. Schiff, O. Hasan, S. Kim, R. Abrams, K. Cosby, B.L. Lambert et al., Diagnostic error in medicine: analysis of 583 physician-reported errors, Arch Intern Med 169 (2009) 1881-1887; P. Croskerry, The importance of cognitive errors in diagnosis and strategies to minimize them, Acad Med 78 (2003) 775-780; H. Singh, A.N.D. Meyer, E.J. Thomas, The frequency of diagnostic errors in outpatient care, BMJ Qual Saf 23 (2014) 727-731; and M.L. Graber, N. Franklin, R. Gordon, Diagnostic error in internal medicine, Arch Intern Med 165 (2005) 1493-1499], emotional harm [J. Conway, F. Federico, K. Stewart, M.J. Campbell, Respectful Management of Serious Clinical Adverse Events, IHI Innovation Series White Paper, IHI, Cambridge, MA, 2011], and inequity [E.N. Chapman, A. Kaatz, M. Carnes, Physicians and implicit bias: how it affects clinical decision making, Acad Med 88 (2013) 354-360; and M. Marmot, R.G. Wilkinson (Eds.), Social Determinants of Health, 2nd ed., Oxford University Press, Oxford, 2005]. No existing safety tool captures the fidelity with which clinicians preserve patient stories [T. Greenhalgh, B. Hurwitz (Eds.), Narrative Based Medicine: Dialogue and Discourse in Clinical Practice, BMJ Books, London, 1998; and W. Levinson, D.L. Roter, J.P. Mullooly, V.T. Dull, R.M. Frankel, Physician-patient communication: the relationship with malpractice claims, JAMA 277 (1997) 553-559]. The stages at which narrative distortion emerges are illustrated in Figure 1. The objective of this article was to define patient narrative distortion as a measurable construct, develop a five-domain taxonomy, propose a scoring system (PNDI), and outline a workflow and validation strategy for clinical use.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted iterative conceptual modeling and structured synthesis of the diagnostic-safety and narrative-medicine literatures [G.D. Schiff, O. Hasan, S. Kim, R. Abrams, K. Cosby, B.L. Lambert et al., Diagnostic error in medicine: analysis of 583 physician-reported errors, Arch Intern Med 169 (2009) 1881-1887; P. Croskerry, The importance of cognitive errors in diagnosis and strategies to minimize them, Acad Med 78 (2003) 775-780; H. Singh, A.N.D. Meyer, E.J. Thomas, The frequency of diagnostic errors in outpatient care, BMJ Qual Saf 23 (2014) 727-731; and M.L. Graber, N. Franklin, R. Gordon, Diagnostic error in internal medicine, Arch Intern Med 165 (2005) 1493-1499] to identify core distortion modes, develop domain definitions, item-level anchors, and scoring thresholds. We propose a multi-phase validation plan including content validity, inter-rater reliability, construct validity, criterion validity, and responsiveness.&lt;/p&gt;&lt;p&gt;&lt;str","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148445098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond bias: using AI to reduce diagnostic noise and manage novelty in clinical reasoning.","authors":"Ahti-Veikko Pietarinen","doi":"10.1515/dx-2026-0010","DOIUrl":"https://doi.org/10.1515/dx-2026-0010","url":null,"abstract":"<p><strong>Objectives: </strong>This study examines AI's capacity to mitigate noise-related diagnostic errors, evaluates its impact on accuracy, and explores the interplay between AI-driven efficiency and human clinical reasoning, particularly in rare or complex cases. Background: Diagnostic errors in clinical reasoning are significantly influenced by noise - random unwanted variability in expert judgments - distinct from cognitive biases. Despite debiasing efforts, noise persists, contributing to adverse events. Artificial intelligence (AI) offers potential solutions but faces limitations in addressing novel diagnostic scenarios requiring creative reasoning.</p><p><strong>Methods: </strong>A narrative review and conceptual analysis synthesises the literature on noise in medical decision-making, AI applications in healthcare, and clinical reasoning frameworks. Case studies (e.g., radiology, pathology) and empirical data on AI performance are reviewed, alongside discussions of noise types (occasion, pattern, group-level) and the role of AI in decision hygiene.</p><p><strong>Results: </strong>AI reduces noise by minimising unwanted variability in pattern recognition tasks (e.g., imaging analysis), improving diagnostic consistency. However, AI struggles with novel hypotheses, creative reasoning, and contextual interpretation, remaining reliant on human oversight. AI's inability to replicate human creativity limits its utility in rare disease diagnosis. Hybrid human-AI systems show promise but require balancing AI noise reduction capacities with human contextual judgment.</p><p><strong>Conclusions: </strong>AI mitigates noise-driven errors in structured tasks but cannot replace human reasoning in complex, uncertain scenarios. Optimal diagnostic accuracy demands the integration of AI's analytical strengths with clinicians' creative and contextual reasoning. Future research should prioritise AI collaboration frameworks and address AI limitations in novelty-driven disease diagnostics.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148430039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects and use of a computerized diagnostic decision support system during the diagnostic process in emergency medicine. A mixed-methods study.","authors":"Stefanie C Hautz, Tori P Epprecht, Thimo Marcin, Karolina Jankovska, Gert Krummrey, Wolf E Hautz","doi":"10.1515/dx-2026-0025","DOIUrl":"https://doi.org/10.1515/dx-2026-0025","url":null,"abstract":"<p><strong>Objectives: </strong>This study's aim was to bridge the gap between the theoretical effectiveness of clinical diagnostic decision support systems (CDDSs) and their practical implementation in emergency medicine. By focusing on one CDDS, we sought to understand how residents use these systems, and how that affects the diagnostic process. Specifically, we investigated the influence of CDDS use on residents' advice-seeking behavior.</p><p><strong>Methods: </strong>This mixed-methods study combined exploratory analyses of data obtained during the Differential Diagnosis Broadening (DDx-BRO) cluster-randomized trial and its accompanying preplanned mixed-methods substudy. The DDx-BRO trial employed a multicenter, outcome assessor, patient-blinded, cluster-randomized, multiperiod crossover design across four Swiss emergency departments. The use of the CDDS in clinical practice was investigated by assessing self-reported use and tracked use, as well as observed workflow indicators such as contact time and advice-seeking behavior.</p><p><strong>Results: </strong>A total of 1,204 patients were included. Observations revealed a significant decrease in consultations with senior physicians in intervention periods, during which 91 % of observed physicians reported using the CDDS because the use was mandatory. When asked, users rated the CDDS as supportive for 23 % the cases. When this was the case, physicians cited the CDDS broadening their list of differential diagnoses and helping confirm their diagnosis as reasons.</p><p><strong>Conclusions: </strong>Our study offers insights into the multifaceted impact of a CDDS on residents' decision-making processes, highlighting the role of such systems in confirmation-oriented diagnostic approaches and changes in consultation patterns.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148430059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use and usefulness of procalcitonin at the emergency department: a 6-month observational study from the University Hospital Centre Zagreb.","authors":"Domagoj Sajfert, Nika Rakuša, Ivana Lapić, Mirna Alvir, Ivan Gornik","doi":"10.1515/dx-2026-0097","DOIUrl":"https://doi.org/10.1515/dx-2026-0097","url":null,"abstract":"<p><strong>Objectives: </strong>The aim of the study was to assess requesting patterns of procalcitonin in adult patients presenting at the Emergency Department (ED), evaluate usefulness of the obtained result on clinical decision-making process in terms of antibiotic prescriptions and determine its diagnostic accuracy for sepsis diagnosis.</p><p><strong>Methods: </strong>Categories of patients in whom procalcitonin was requested were based on clinical, imaging and laboratory data, and were classified as follows: uncertain cause of fever in patients without major comorbidities, with cancer, in immunosupressed patients, evaluation of disease severity when infection is evident and unclear reason for testing. Usefulness of procalcitonin determination was evaluated by two senior physicians according to all available clinical data. Impact of procalcitonin results on decision-making process was assessed by analysis of antibiotic prescriptions. Diagnostic sensitivity and specificity of procalcitonin for sepsis diagnosis were determined at the cut-off of 2.0 μg/L.</p><p><strong>Results: </strong>Procalcitonin is mostly ordered due to unexplained fever, but in one third of patients is ordered with evident infection or for unclear reason. Procalcitonin was considered useful in 25 % of patients, regardless of the reason for determination (p=0.688). Patients were equally likely to receive antibiotic treatment, regardless of procalcitonin concentrations (p=0.846). Procalcitonin revealed 35 % sensitivity and 84 % specificity for sepsis diagnosis.</p><p><strong>Conclusions: </strong>Despite frequent ordering of procalcitonin in patients with fever at the ED, the obtained result is not a decisive factor for further patient management. Procalcitonin alone has limited ability in distinguishing infectious from non-infectious causes of fever in patients presenting at the ED.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148430074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic probability change across follow-up: a Bayesian EHR cohort study in psychiatry.","authors":"Yu Chang, Si-Sheng Huang, Wen-Yu Hsu, Yi-Chun Liu","doi":"10.1515/dx-2026-0023","DOIUrl":"https://doi.org/10.1515/dx-2026-0023","url":null,"abstract":"<p><strong>Objectives: </strong>Diagnosis is often interpreted through a Bayesian lens as a point-in-time update from prior to posterior probability after new clinical evidence. In routine care, however, diagnostic evidence may accumulate across repeated encounters. This study applied a Bayesian EHR-based framework to estimate diagnostic probability change from the initial to final observed visit across major psychiatric diagnostic categories.</p><p><strong>Methods: </strong>We analyzed electronic health records from 17,057 psychiatric outpatients at Changhua Christian Hospital from 2016 to 2024. A multivariate Bayesian generalized linear mixed model was used to estimate the final-versus-initial change in diagnostic recording probability across 10 major ICD-10 categories, controlling for demographics, visit frequency, and within-patient correlations.</p><p><strong>Results: </strong>The final-versus-initial odds-scale effect varied substantially across diagnostic categories. The largest effects were observed for F7x (Intellectual developmental disorder; Odds Ratio [OR]=7.4, 95 % Credible Interval [CrI] 5.2-10.8) and F9x (Behavioural and emotional disorders with onset in childhood/adolescence; OR=5.7, 95 % CrI 4.5-7.3), whereas F4x showed the smallest effect (OR=1.2, 95 % CrI 1.1-1.4). When translated to the probability scale under specified baseline probabilities, these odds-scale differences corresponded to larger probability changes for diagnoses requiring longitudinal developmental, functional, or collateral information.</p><p><strong>Conclusions: </strong>This framework provides a probability-scale approach for characterizing diagnostic updating across follow-up in routine EHR data. It may help identify diagnostic categories in which longitudinal evidence accumulation is especially important and inform the design of structured follow-up and assessment workflows.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148430049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lessons in clinical reasoning: a case of a boy who continued crying after falling.","authors":"Mizuka Fu, Toshiki Nakamura, Shun Kishibe, Yusuke Hagiwara, Taro Shimizu, Hiroshi Sakakibara","doi":"10.1515/dx-2025-0174","DOIUrl":"10.1515/dx-2025-0174","url":null,"abstract":"<p><strong>Objectives: </strong>Diagnostic errors are multifactorial and tend to occur frequently in the emergency department. Pediatric emergency care presents unique challenges, particularly in the evaluation of trauma in preverbal infants who cannot adequately describe their symptoms. Herein, we present the case of an infant in whom irritability following minor head trauma was incorrectly diagnosed.</p><p><strong>Case presentation: </strong>A 1-year-6-month-old boy presented to the emergency department with irritability following minor head trauma. The initial evaluation focused on the head trauma, and the child's irritability was attributed to an upper respiratory tract infection and the effect of the unfamiliar examination environment. The patient was discharged, but several hours later, he returned because of right thigh swelling, which revealed a femoral fracture. Root cause analysis using a fishbone diagram identified the contributing factors to the delayed diagnosis as the vague, clinical presentation of pediatric trauma, multiple cognitive biases, and the role of family concerns in clinical reasoning.</p><p><strong>Conclusions: </strong>In preverbal children, behavioral changes, such as a sudden refusal to walk, may indicate a serious pathology. Clinicians should avoid anchoring, perform a thorough, whole-body assessment, and take the family's concerns into consideration to ensure diagnostic accuracy in cases of pediatric trauma.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":"150-156"},"PeriodicalIF":1.9,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148387571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intraloop neoangiogenesis in an AI-classified scleroderma pattern: recognizing a morphological clue to suspected dermatomyositis.","authors":"Angelo Nigro","doi":"10.1515/dx-2026-0062","DOIUrl":"https://doi.org/10.1515/dx-2026-0062","url":null,"abstract":"<p><strong>Objectives: </strong>To describe a diagnostic near miss in AI-assisted nailfold capillaroscopy, illustrating how a correct AI-based classification as an early scleroderma pattern may still lead to diagnostic error if not integrated with additional morphological findings, such as intraloop neoangiogenesis, which should prompt consideration of dermatomyositis.</p><p><strong>Case presentation: </strong>A 50-year-old woman with episodic acrocyanosis underwent nailfold videocapillaroscopy (NVC). AI-assisted analysis classified the pattern as \"early scleroderma\". Expert review identified branching neoangiogenesis within the same capillary loop as a giant capillary. This composite finding is not typical of systemic sclerosis microangiopathy and lies outside the scope of current algorithms, which are trained on scleroderma-based patterns and are not designed to recognize alternative microangiopathic processes. Accordingly, the system did not flag this feature as diagnostically relevant. This prompted immunological testing, which showed anti-Mi-2 positivity on repeat testing, with negative systemic sclerosis-specific autoantibodies, raising suspicion for dermatomyositis.</p><p><strong>Conclusions: </strong>AI-assisted capillaroscopy provides accurate pattern classification within its training framework but does not replace integrative morphological reasoning. When dominant features such as giant capillaries drive classification, clinicians must assess internal consistency and consider additional findings beyond pattern assignment. Neoangiogenesis is a diagnostically relevant feature that, in the appropriate context, should prompt consideration of dermatomyositis. Diagnostic error reflects not algorithm failure but limits of training scope and over-reliance on pattern-based outputs. Expert interpretation remains essential.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148390138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy of emergency physicians' probability estimates for acute coronary syndrome.","authors":"Mia Hemborg Kristiansson, Torgny Wessman, Ulf Ekelund","doi":"10.1515/dx-2026-0020","DOIUrl":"10.1515/dx-2026-0020","url":null,"abstract":"<p><strong>Objectives: </strong>Accurate risk assessment is crucial for appropriate management of patients presenting to the emergency department (ED) with chest pain. This study aimed to evaluate how accurately emergency physicians estimate the probability of acute coronary syndrome (ACS).</p><p><strong>Methods: </strong>A vignette-based on a real ED chest pain patient was presented in four sequential frames with increasing clinical information: history and symptoms, ECG findings, and elevated troponin levels. Evidence-based reference probabilities of ACS were established for each frame. Emergency physicians in the Skåne region of Sweden were invited to estimate the probability of ACS at each stage. The primary outcome was the difference between physicians' estimates and the reference probabilities.</p><p><strong>Results: </strong>Of 707 respondents, 357 physicians with recent ED experience provided complete responses and were included in the analysis. The median age was 34 years (IQR 30-40), 56 % were male, and slightly more than half were specialists or senior residents. Physicians consistently overestimated the probability of ACS across all frames. The largest discrepancy occurred in the initial history/symptoms frame, where the median estimated probability was approximately 70 %, compared with a reference range of 3-16 %. Although estimation accuracy improved as additional diagnostic information was provided, estimates remained higher than reference probabilities throughout.</p><p><strong>Conclusions: </strong>In this realistic staged case, ED physicians substantially overestimated the risk of ACS in chest pain patients. Such overestimation may contribute to unnecessary diagnostic testing and overtreatment, highlighting the need for improved training and/or decision-support tools in emergency care.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148374803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}