Radiography最新文献

{"title":"Response to 'Letter to the editor: Use of ChatGPT in radiological dose management'.","authors":"L Federico, D D Fusaro, G C Coppola, M Gregori, S Durante","doi":"10.1016/j.radi.2025.103164","DOIUrl":"https://doi.org/10.1016/j.radi.2025.103164","url":null,"abstract":"","PeriodicalId":47416,"journal":{"name":"Radiography","volume":" ","pages":"103164"},"PeriodicalIF":2.8,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145058747","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":"Evaluation of paediatric head CT radiation dose in Jordan: A new national diagnostic reference level survey","authors":"S.F. Hamedat ,&nbsp;Y. Md Radzi ,&nbsp;M.D. Salman ,&nbsp;M.A. Al-Shipli","doi":"10.1016/j.radi.2025.103158","DOIUrl":"10.1016/j.radi.2025.103158","url":null,"abstract":"<div><h3>Introduction</h3><div>Paediatric head CT scans are essential for diagnosing various neurological conditions. However, they pose radiation risks due to children's heightened sensitivity to ionizing radiation. The absence of standardized national dose–response lines (DRLs) in Jordan contributes to considerable dose variability across hospitals.</div></div><div><h3>Methods</h3><div>This retrospective multicenter study evaluated 1550 pediatric head CT examinations performed between February and November 2024 in eight Jordanian hospitals. Patients were grouped into four age categories: &lt;1, 1–5, 5–10, and 10–15 years. Radiation dose metrics, including CTDI_vol and DLP, were collected. DRLs were established based on the 75th percentile of dose distributions. Statistical analysis was performed using SPSS to assess inter-hospital variability. DRLs were established using age-based categories, as patient weight data were not consistently available across hospitals, which is acknowledged as a methodological limitation.</div></div><div><h3>Results</h3><div>Substantial differences in CTDI_vol and DLP values were observed across hospitals and age groups. For infants (&lt;1 year), median DLP values ranged from 409.6 to 550.9 mGy cm. Hospitals B and D recorded the highest radiation levels, while Hospitals F and H consistently reported lower values across all age groups.</div></div><div><h3>Conclusion</h3><div>The findings highlight an urgent need for national dose standardization in pediatric CT imaging in Jordan. Establishing local DRLs based on international benchmarks is critical to ensure safe and consistent practices.</div></div><div><h3>Implications for Practice</h3><div>This study underscores the need to improve radiation safety in paediatric CT imaging across Jordan. Radiology staff should receive ongoing training in paediatric dose optimization, including the use of age- and size-specific protocols, automated exposure control, and iterative or deep learning reconstruction techniques. Furthermore, implementing protocol standardization and national regulatory oversight will help ensure adherence to safe radiation practices.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":"31 6","pages":"Article 103158"},"PeriodicalIF":2.8,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048452","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":"Reported patient safety incidents in radiology – understanding the relation between risk areas and their underlying factors","authors":"A. Wallin,&nbsp;M. Lundén,&nbsp;M. Ringdal,&nbsp;K. Ahlberg,&nbsp;M. Bazzi","doi":"10.1016/j.radi.2025.103149","DOIUrl":"10.1016/j.radi.2025.103149","url":null,"abstract":"<div><h3>Introduction</h3><div>Learning from errors and underlying causes creates important knowledge, enabling re-evaluation of routines and preventing recurrence of errors. Because of the nature of activities in radiology, measurement of specific risk areas is needed. The aim of this study was to analyse reported events in radiology and identify the underlying causes in relation to the risk.</div></div><div><h3>Methods</h3><div>Patient Safety Reporting System (PSRS) data from three radiological clinics were retrieved, covering 923 risk events. The reports were studied deductively based on a risk assessment framework (i.e. risk areas) using qualitative content analysis.</div></div><div><h3>Results</h3><div>The events were distributed between six risk areas covering risks that “the patient could be exposed to unnecessary radiation” (7 %); “the patient could receive an inaccurate diagnosis” (26 %); “the patient could incur drug-induced damage” (2 %); “the patient could suffer direct physical injury” (9 %); “the patient's examination and treatment could be delayed or not carried out” (47 %); and that “the patient's general health condition could deteriorate” (9 %). Twenty-two subcategories were identified and linked to these risk areas, representing underlying causes.</div></div><div><h3>Conclusion</h3><div>This study validates a previously designed risk assessment framework for radiology. Collaboration complexities between internal and external actors are evident, particularly when utilizing external practitioners and teleradiology services. Because of capacity issues, radiological expertise is often not given due regard, and risks escalate when information systems fail to meet radiology documentation requirements. To improve patient safety, radiological competence needs to be maintained, established standards and quality controls must be followed, and documentation and reporting need to be accurate.</div></div><div><h3>Implications for practice</h3><div>An awareness of long-term effects of errors in radiology needs to be created, and collaboration challenges for improved patient safety and well-timed diagnoses need to be solved.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":"31 6","pages":"Article 103149"},"PeriodicalIF":2.8,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026462","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":"Optimization of carotid CT angiography image quality with deep learning image reconstruction with high setting (DLIR-H) algorithm under ultra-low radiation and contrast agent conditions","authors":"C. Wang ,&nbsp;J. Long ,&nbsp;X. Liu ,&nbsp;W. Xu ,&nbsp;H. Zhang ,&nbsp;Z. Liu ,&nbsp;M. Yu ,&nbsp;C. Wang ,&nbsp;Y. Wu ,&nbsp;A. Sun ,&nbsp;K. Xu ,&nbsp;Y. Meng","doi":"10.1016/j.radi.2025.103154","DOIUrl":"10.1016/j.radi.2025.103154","url":null,"abstract":"<div><h3>Introduction</h3><div>Carotid artery disease is a major cause of stroke and is frequently evaluated using Carotid CT Angiography (CTA). However, the associated radiation exposure and contrast agent use raise concerns, particularly for high-risk patients. Recent advances in Deep Learning Image Reconstruction (DLIR) offer new potential to enhance image quality under low-dose conditions. This study aimed to evaluate the effectiveness of the DLIR-H algorithm in improving image quality of 40 keV Virtual Monoenergetic Images (VMI) in dual-energy CTA (DE-CTA) while minimizing radiation dose and contrast agent usage.</div></div><div><h3>Methods</h3><div>A total of 120 patients undergoing DE-CTA were prospectively divided into four groups: one control group using ASIR-V and three experimental groups using DLIR-L, DLIR-M, and DLIR-H algorithms. All scans employed a “triple-low” protocol—low radiation, low contrast volume, and low injection rate. Objective image quality was assessed via CT values, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Subjective image quality was evaluated using a 5-point Likert scale.</div></div><div><h3>Results</h3><div>The DLIR-H group showed the greatest improvements in image quality, with significantly reduced noise and increased SNR and CNR, particularly at complex vascular sites such as the carotid bifurcation and internal carotid artery. Radiation dose and contrast volume were reduced by 15.6 % and 17.5 %, respectively. DLIR-H also received the highest subjective image quality scores.</div></div><div><h3>Conclusion</h3><div>DLIR-H significantly enhances DE-CTA image quality under ultra-low-dose conditions, preserving diagnostic detail while reducing patient risk.</div></div><div><h3>Implications for practitioners</h3><div>DLIR-H supports safer and more effective carotid imaging, especially for high-risk groups like renal-impaired patients and those needing repeated scans, enabling wider clinical use of ultra-low-dose protocols.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":"31 6","pages":"Article 103154"},"PeriodicalIF":2.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996852","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":"A cross-sectional path analysis of turnover intentions among Philippine radiographers","authors":"M.A.D. Ganzon ,&nbsp;N.P. Calaguas","doi":"10.1016/j.radi.2025.103151","DOIUrl":"10.1016/j.radi.2025.103151","url":null,"abstract":"<div><h3>Introduction</h3><div>Radiographer turnover poses a major challenge for healthcare systems, especially in low-to-middle-income countries like the Philippines. Shortages are worsened by low licensure pass rates, limited career advancement, and uneven workforce distribution. This study offers the first model-based analysis of turnover intention predictors among Filipino radiographers, addressing a critical gap in allied health workforce research.</div></div><div><h3>Methods</h3><div>A cross-sectional survey was conducted among 239 licensed radiographers in various Philippine healthcare settings. Validated measures assessed measured supervisory coaching behavior, job satisfaction, job performance, organizational commitment, career commitment, and turnover intention. Structural path analysis using JASP assessed direct effects, with model fit evaluated via Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR).</div></div><div><h3>Results</h3><div>The model showed excellent fit (CFI = 1.000, TLI = 1.000, RMSEA = 0.000, SRMR ≈ 0.000). Organizational commitment was the strongest negative predictor of turnover intention (β = −0.645, p &lt; .001), followed by career commitment (β = −0.206, p &lt; .001) and job satisfaction (β = −0.146, p = 0.002). While supervisory coaching and job performance were significantly correlated with turnover intention, they were not direct predictors in the model.</div></div><div><h3>Conclusions</h3><div>This study offers new evidence that affective and motivational factors, particularly organizational and career commitment, are more crucial to retention than performance metrics. It emphasizes the importance of fostering emotional investment and professional identity in healthcare staff.</div></div><div><h3>Implications for Practice</h3><div>Healthcare leaders should develop strategies that strengthen organizational belonging, career development, and satisfaction to reduce attrition. The results support context-specific retention policies and point to the need for future longitudinal research on indirect effects and trends.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":"31 6","pages":"Article 103151"},"PeriodicalIF":2.8,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989161","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":"Predicting first-trimester pregnancy outcome in threatened miscarriage: A comparison of a multivariate logistic regression and machine learning models","authors":"L. Sammut ,&nbsp;P. Bezzina ,&nbsp;V. Gibbs ,&nbsp;Y. Muscat-Baron ,&nbsp;A. Agius-Camenzuli ,&nbsp;J. Calleja-Agius","doi":"10.1016/j.radi.2025.103159","DOIUrl":"10.1016/j.radi.2025.103159","url":null,"abstract":"<div><h3>Introduction</h3><div>Threatened miscarriage (TM), defined as first-trimester vaginal bleeding with a closed cervix and detectable fetal cardiac activity, affects up to 30 % of clinically recognised pregnancies and is linked to increased risk of adverse outcomes. This study evaluates the predictive value of first-trimester ultrasound (US) and biochemical (BC) markers in determining outcomes among women with TM symptoms.</div></div><div><h3>Methods</h3><div>This prospective cohort study recruited 118 women with viable singleton pregnancies (5⁺⁰ to 12⁺⁶ weeks' gestation) from Malta's national public hospital between January 2023 and June 2024. Participants underwent US and BC assessment, along with collection of clinical and sociodemographic data. Pregnancy outcomes were followed to term and classified as live birth or loss. Univariate logistic regression identified individual predictors. Multivariate logistic regression (MLR) and random forest (RF) modelling assessed combined predictive performance.</div></div><div><h3>Results</h3><div>Among 118 TM cases, 77 % resulted in live birth, 23 % in loss. MLR identified progesterone, cervical length, mean gestational sac diameter (MGSD), trophoblast thickness, sFlt-1:PlGF ratio, and maternal age as significant predictors. Higher progesterone, cervical length, MGSD, and sFlt-1:PlGF ratio reduced risk, while maternal age over 35 increased it. MLR achieved 82.7 % accuracy (AUC = 0.89). RF improved accuracy to 93.1 % (AUC = 0.97), confirming the combined predictive value of US and BC markers.</div></div><div><h3>Conclusion</h3><div>US and BC markers hold predictive value in TM. Machine learning, particularly RF, may improve early clinical risk stratification.</div></div><div><h3>Implications for practice</h3><div>This tool may support timely decision-making and personalised monitoring, intervention, and counselling for women with TM.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":"31 6","pages":"Article 103159"},"PeriodicalIF":2.8,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989160","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":"How physically active are women undergoing radiation therapy for breast cancer? A cross-sectional study of patterns, attitudes, and support needs.","authors":"L Feighan, L MacDonald-Wicks, R Callister, Y Surjan","doi":"10.1016/j.radi.2025.103146","DOIUrl":"https://doi.org/10.1016/j.radi.2025.103146","url":null,"abstract":"<p><strong>Introduction: </strong>Breast cancer is the most common cancer worldwide, with 2.3 million diagnoses each year. Treatment options like surgery, chemotherapy, and radiation therapy (RT) can cause side effects and reduce quality of life. In Australia, the 5-year survival rate for breast cancer is 92 %, indicating a need for effective support strategies during treatment. Physical activity (PA) has proven benefits for both physical and emotional health and can prevent chronic illnesses. This study explores the patterns of PA, attitudes, and support needs of women undergoing RT for breast cancer.</p><p><strong>Methods: </strong>An online survey was circulated among radiation oncology sites in Australia's Capital Territory, New South Wales, and Queensland. The survey included 70 questions about well-being, PA patterns, and support needs.</p><p><strong>Results: </strong>Ninety women participated in the study, most aged 45 to 74, who had a lumpectomy before adjuvant treatment. During RT, 56 % reported engaging in less PA than desired, and 61 % experienced decreased attentiveness in work and daily activities due to the illness's impact on their mental health. On average, participants briskly walked four times a week both before diagnosis and during RT. While vigorous activities increased from once a week before diagnosis to three times a week during RT. Additionally, 30 % felt they received inadequate information about PA during RT.</p><p><strong>Conclusion: </strong>This study identified the PA habits of women with breast cancer receiving RT and the need for support in this area. PA could enhance patient well-being during cancer treatment; however, further investigation is required to identify appropriate methods for implementing PA interventions during RT.</p><p><strong>Implications for practice: </strong>Despite the increasing evidence supporting the benefits of PA in cancer care, a significant knowledge gap persists among oncology practitioners providing PA guidance to patients. This presents an opportunity for implementing training in this area.</p>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":" ","pages":"103146"},"PeriodicalIF":2.8,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144993883","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":"Sustainable four-dimensional cardiac CT: Minimizing data storage via reduced matrix size with optimized reconstruction","authors":"H. Nakajima,&nbsp;T. Nishii,&nbsp;S. Okuyama,&nbsp;T. Tanaka,&nbsp;T. Namboku,&nbsp;K. Hara,&nbsp;T. Hayashi,&nbsp;T. Morikawa,&nbsp;Y. Ohta,&nbsp;T. Fukuda","doi":"10.1016/j.radi.2025.103156","DOIUrl":"10.1016/j.radi.2025.103156","url":null,"abstract":"<div><h3>Introduction</h3><div>The increasing volume of four-dimensional cardiac CT data complicates transcatheter aortic valve repair (TAVR) planning, particularly regarding data storage. This study evaluates a modified 256 × 256 reconstruction method for TAVR-CT that reduces storage requirements while maintaining aortic valve measurement accuracy.</div></div><div><h3>Methods</h3><div>A retrospective analysis was performed on 75 TAVR-CT scans obtained using the dual-source energy-integrating detector CT. The modified 256 × 256 reconstruction used a sharper Bv44 kernel (iterative construction strength 4) and narrowed the field of view to 80 %. We assessed virtual basal ring (BR) areas using reference 512 × 512, standard 256 × 256, and modified 256 × 256 images. Five technologists—three from the CT team and two from the interventional radiology (IR) team—manually evaluated the BR areas. The measurements were averaged within each team. The intraclass correlation coefficient (ICC), bias, and consistency in device selection were assessed.</div></div><div><h3>Results</h3><div>Among 75 patients (48 women, median age 85), the mean BR area was 416.2 ± 77.9 mm². The mean bias between the modified 256 × 256 and reference 512 × 512 images was −2.5 mm² (95 % CI: −5.2 to 0.3), significantly smaller (P = 0.04) than the bias between standard 256 × 256 and reference (4.1 mm², 95 % CI: 0.7 to 7.5). BR measurements demonstrated excellent reproducibility (ICC = 0.996–0.997), with limits of agreement (LOA) for the modified 256 × 256 ranging from −13.0 to 12.2 mm² for the IR team and −14.5 to 11.6 mm² for the CT team, comparable to the inter-team LOA of −18.1 to 13.1 mm². Valve selection consistency was 95 % (Cohen's Kappa = 0.92).</div></div><div><h3>Conclusion</h3><div>The modified 256 × 256 reconstruction method effectively preserved BR measurements and valve selection reliability.</div></div><div><h3>Implications for practice</h3><div>The proposed 256 × 256 method could cut data storage while ensuring accurate valve selection.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":"31 6","pages":"Article 103156"},"PeriodicalIF":2.8,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933106","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":"Comment on \"Application of ChatGPT 4.0 in radiological dose management\" by Wu et al.","authors":"M Abuzaid","doi":"10.1016/j.radi.2025.103155","DOIUrl":"https://doi.org/10.1016/j.radi.2025.103155","url":null,"abstract":"","PeriodicalId":47416,"journal":{"name":"Radiography","volume":" ","pages":"103155"},"PeriodicalIF":2.8,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144993915","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":"Convolutional neural network application for automated lung cancer detection on chest CT using Google AI Studio.","authors":"Z Aljneibi, S Almenhali, L Lanca","doi":"10.1016/j.radi.2025.103152","DOIUrl":"https://doi.org/10.1016/j.radi.2025.103152","url":null,"abstract":"<p><strong>Introduction: </strong>This study aimed to evaluate the diagnostic performance of an artificial intelligence (AI)-enhanced model for detecting lung cancer on computed tomography (CT) images of the chest. It assessed diagnostic accuracy, sensitivity, specificity, and interpretative consistency across normal, benign, and malignant cases.</p><p><strong>Methods: </strong>An exploratory analysis was performed using the publicly available IQ-OTH/NCCD dataset, comprising 110 CT cases (55 normal, 15 benign, 40 malignant). A pre-trained convolutional neural network in Google AI Studio was fine-tuned using 25 training images and tested on a separate image from each case. Quantitative evaluation of diagnostic accuracy and qualitative content analysis of AI-generated reports was conducted to assess diagnostic patterns and interpretative behavior.</p><p><strong>Results: </strong>The AI model achieved an overall accuracy of 75.5 %, with a sensitivity of 74.5 % and specificity of 76.4 %. The area under the ROC curve (AUC) for all cases was 0.824 (95 % CI: 0.745-0.897), indicating strong discriminative power. Malignant cases had the highest classification performance (AUC = 0.902), while benign cases were more challenging to classify (AUC = 0.615). Qualitative analysis showed the AI used consistent radiological terminology, but demonstrated oversensitivity to ground-glass opacities, contributing to false positives in non-malignant cases.</p><p><strong>Conclusion: </strong>The AI model showed promising diagnostic potential, particularly in identifying malignancies. However, specificity limitations and interpretative errors in benign and normal cases underscore the need for human oversight and continued model refinement.</p><p><strong>Implications for practice: </strong>AI-enhanced CT interpretation can improve efficiency in high-volume settings but should serve as a decision-support tool rather than a replacement for expert image review.</p>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":" ","pages":"103152"},"PeriodicalIF":2.8,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144993918","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}