Application of Patient Version of General Practitioner Assessment of Cognition for Screening Elderly with Mild Cognitive Impairment in Community by YANG Ting, HE Jiali, SONG Linlin, LIU Wentao, GUI Chenfan, CHEN Xiao, XU Li, JIA Chengsen

“…The sensitivity was 82.7%, the specificity was 72.6%, the positive predictive value was 57.2%, and the negative predictive value was 90.4%.ConclusionThe P-GPCOG is a good community-based cognitive screening tool for screening MCI patients in community, but its original scoring criteria needs to be refined to improve the correct screening rate.…”
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Preoperative Risk Assessment of Lymph Node Metastasis in cT1 Lung Cancer: A Retrospective Study from Eastern China by Chengyan Zhang, Guanchao Pang, Chengxi Ma, Jingni Wu, Pingli Wang, Kai Wang

“…In order to assess lymph node status before operation, we aim to develop a noninvasive predictive model using preoperative clinical information. …”
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Translation, adaptation and validation of an epilepsy screening instrument in two Ghanaian languages. by Emmanuel Kwame Darkwa, Sabina Asiamah, Elizabeth Awini, Cynthia Sottie, Anthony Godi, John E Williams, Albert Akpalu, J Helen Cross, Josemir W Sander, Arjune Sen, Charles R Newton, Anthony Danso-Appiah, Patrick Adjei

“…It excluded epilepsy with negative predictive values of 97.9% and 95.9% for the two stages.…”
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Determinants of Chronic Kidney Disease among Young Adults attending a Tertiary Care Hospital in Madurai- A Case-Control Study by Sridevi PN, Thirukumaran R, Kannan Muthuraman A, Arunkumar Yograj, Priya S, Hemamalini B

“…In addition this study predicted that, rural population was more vulnerable to CKD, so the screening service to these populations through primary health care approach is essential. …”
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Vocational and psychosocial predictors of medical negligence claims among Australian doctors: a prospective cohort analysis of the MABEL survey by Anthony Scott, Marie Bismark, Owen M Bradfield, Matthew Spittal

“…Increasing age predicted claims more strongly in male doctors. Personality type predicted claims in both male and female doctors.Conclusions Modifiable risk factors contribute to an increased risk of medical negligence claims among doctors in Australia. …”
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Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors by Md Abdullah Al Mehedi, Shah Saki, Krutikkumar Patel, Chaopeng Shen, Sagy Cohen, Virginia Smith, Adnan Rajib, Emmanouil Anagnostou, Tadd Bindas, Kathryn Lawson

“…We show that predictable time variability explains a large fraction (∼35%) of n variance compared to spatial variability (50%). …”
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Identification of Cytosolic DNA Sensor cGAS-STING as Immune-Related Risk Factor in Renal Carcinoma following Pan-Cancer Analysis by Zheng Wu, Ying Lin, Li-Min Liu, Yan-Li Hou, Wei-Ting Qin, Lei Zhang, Shu-Heng Jiang, Qin Yang, Yong-Rui Bai

“…Kaplan–Meier curves, Cox regression analyses, and the area under the curve (AUC) were employed to decipher the predictive value of cGAS-STING risk score and TIICs across several human cancers. …”
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Diagnostic efficacy and necessity of 18F-FDG PET/CT in fever of unknown origin: insights from a retrospective cohort study by Xiaoman Yu, Shuang Wang, Na Du, Hongguang Zhao, Haiying Chen

“…The final diagnoses of 136 patients (47.9%) correlated with their 18F-FDG PET/CT results, yielding a sensitivity of 79.5%, specificity of 61.1%, positive predictive value of 75.6%, and negative predictive value of 66.3%. …”
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Validity of the scoring system for traumatic liver injury: a generalized estimating equation analysis by Kangho Lee, Dongyeon Ryu, Hohyun Kim, Chang Ho Jeon, Jae Hun Kim, Chan Yong Park, Seok Ran Yeom

“…Purpose The scoring system for traumatic liver injury (SSTLI) was developed in 2015 to predict mortality in patients with polytraumatic liver injury. …”
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READMISSIONS TO ADOLESCENT PSYCHIATRIC INPATIENT CARE: A REGISTER STUDY by VISA VADÉN, RIITTAKERTTU KALTIALA, TIMO HOLTTINEN

“…However, female gender predicted readmissions in general and within 30 days when disorder-related factors were accounted for. …”
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The MRI-based vertebral bone quality score is a predictor of pedicle screw loosening following instrumented posterior lumbar fusion by Yung-Hsueh Hu, Jian-Hong Chou, Yu-Cheng Yeh, Ming-Kai Hsieh, Tsung-Ting Tsai, Wen-Jer Chen, Lih-Hui Chen, Po-Liang Lai, Chi-Chien Niu

“…The VBQ score significantly predicted screw loosening with an accuracy of 78.9%. …”
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Corticospinal Tract Wiring and Brain Lesion Characteristics in Unilateral Cerebral Palsy: Determinants of Upper Limb Motor and Sensory Function by Cristina Simon-Martinez, Ellen Jaspers, Lisa Mailleux, Els Ortibus, Katrijn Klingels, Nicole Wenderoth, Hilde Feys

“…Here, we first investigated whether structural brain lesion characteristics could predict the underlying CST wiring and we explored the role of CST wiring and brain lesion characteristics to predict UL motor and sensory functions in uCP. …”
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Value of Plasma NGAL and Creatinine on First Day of Admission in the Diagnosis of Cardiorenal Syndrome Type 1 by Hao Phan Thai, Bao Hoang Bui, Tien Hoang Anh, Minh Huynh Van

“…The optimal cutoff plasma NGAL for diagnosing CRS1 was >353.23 ng/ml, area under curve (AUC) 0.732 (95% CI 0.65–0.80, p<0.001), sensitivity 74.47%, specificity 68.48%, positive predictive value 54.7%, and negative predictive value 84%. …”
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Exploring the significance of medical humanities in shaping internship performance: insights from curriculum categories by Chao Ting Chen, Anna Y.Q. Huang, Po-Hsun Hou, Ji-Yang Lin, His-Han Chen, Shiau-Shian Huang, Stephen J. H. Yang

“…Ten-fold cross-validation machine learning models (support vector machines, logistic regression, random forest) were performed to predict the internship grades. In addition, multiple variables regression was done to know the independent impact of MH on internship grades.Results MH showed the important roles in predicting internship performance in the machine learning model, with substantially reduced predictive accuracy after excluding MH variables (e.g. …”
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Evaluating the utility of the HAS‐BLED bleeding‐estimator tool for transurethral resection of prostate by Lu Yu Kuo, Zhong Li Titus Lim, Caitlin Letch, Joshua Silverman, Jason Jae Yeun Kim, Scott McClintock

“…Abstract Objectives To evaluate the utility of the HAS‐BLED bleeding risk‐estimation tool to predict for clinically significant postoperative haematuria in patients receiving transurethral resection of prostate (TURP). …”
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The association between neutrophil to high-density lipoprotein cholesterol ratio and gallstones: a cross-sectional study by Yinkang Wang, Xingchen Shang, Yinchao Zhang, Yu Zhang, Wei Shen, Qian Wu, Wenyi Du

“…The area under the curve (AUC) of the ROC curve was 0.550, indicating that NHR has a predictive value for the development of gallstones. …”
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Explainable Self-Supervised Dynamic Neuroimaging Using Time Reversal by Zafar Iqbal, Md. Mahfuzur Rahman, Usman Mahmood, Qasim Zia, Zening Fu, Vince D. Calhoun, Sergey Plis

“…Objective: Functional magnetic resonance imaging data pose significant challenges due to their inherently noisy and complex nature, making traditional statistical models less effective in capturing predictive features. While deep learning models offer superior performance through their non-linear capabilities, they often lack transparency, reducing trust in their predictions. …”
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Dynamic Monitoring of sTREM-1 and Other Biomarkers in Acute Cholangitis by Jiahui Jiang, Xiaolei Wang, Tongtong Cheng, Mingyue Han, Xinxin Wu, Haitong Wan, DaojunYu

“…CRP had the highest AUC to identify AC from individuals without AC or other infections (AUC 1.000, sensitivity 100.0%, specificity 100.0%, positive predictive value 100.0%, and negative predictive value 100.0%). …”
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CT Texture Analysis in Breast Cancer Patients Undergoing CT-Guided Bone Biopsy: Correlations With Histopathology by Silvio Wermelskirchen, Jakob Leonhardi, Anne-Kathrin Höhn, Georg Osterhoff, Nikolas Schopow, Susanne Briest, Timm Denecke, Hans-Jonas Meyer

“…There is the potential to predict the immunohistochemical subtype with a high degree of accuracy. …”
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Patient–specific hemodynamic modeling to optimize LVAD speed and right heart health by Mustafa M. Ahmed, Holly Grant, Jasmine Martinez, Joshua Thomas, Mohammad Al-Ani, Alex Parker, Juan Vilaro, Juan Aranda, Venkat Keshav Chivukula

“…Using each patient’s customized VHM, virtual speed optimization demonstrated congruence with clinician-guided optimization, with a median predicted speed increase of 321 rpm. After virtual optimization, there was a decrease in the pulmonary artery pressure for 13 patients (81.25%), indicating a predicted improvement in pulmonary parameters. …”
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