Predictive biomarkers of survival in patients with advanced hepatocellular carcinoma receiving atezolizumab plus bevacizumab treatment by Young Eun Chon, Jaekyung Cheon, Hyeyeong Kim, Beodeul Kang, Yeonjung Ha, Do young Kim, Seong Gyu Hwang, Hong Jae Chon, Beom Kyung Kim

“…Abstract Background Since atezolizumab plus bevacizumab (ATE+BEV) regimen for patients with unresectable hepatocellular carcinoma (HCC) was released quite recently, real‐world data are lacking. We evaluated efficacy, safety, and predictive biomarkers for survival in patients receiving ATE+BEV. …”
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Predictive value of an early amplitude-integrated electroencephalogram for short-term neurologic outcomes in preterm infants by Kyung Hee Park, Young Mi Kim, Yun-Jin Lee, Mi Hye Bae, Na Rae Lee, Young Mi Han, Hye-Young Kim, Su Young Kim, Shin Yun Byun

“…The aim of this study was to compare serial scores of amplitude-integrated electroencephalography (aEEG) in preterm infants with favorable neurologic outcome compared with those with unfavorable neurologic outcome and to evaluate whether aEEG in the early days of life has predictive value for short-term neurologic outcome in preterm infants. …”
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Predictive Value of 5-Methoxytryptophan on Clinical Outcome in Patients with Sepsis Associated Acute Kidney Injury by Sun X, Liu Y, Liu Y, Ai S, Luo P

“…Additionally, we seek to evaluate the predictive value of 5-MTP in long-term clinical prognosis following SA-AKI.Patients and Methods: A prospective cohort study included 31 healthy controls and 78 patients diagnosed with sepsis at the Affiliated Hospital of Qinghai University. …”
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Predictive score for in-hospital mortality in patients with severe acute exacerbations of chronic obstructive pulmonary disease by Yanlu Hu, Yanyan Li, Zhenzhen Xing, Yang Cao, Huanyu Long, Yanfei Guo

“…The aim of this study was to establish a predictive score for assessing the risk of death in patients with severe acute exacerbations of COPD (AECOPD) to help clinicians evaluate the condition and prognosis of patients. …”
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Predictive Model for Early Neurological Deterioration in Acute Ischemic Stroke Utilizing Novel Thrombotic Biomarkers by Yifei Zhao, Hao Zhu, Changfei Dai, Wen Liu, Wenjin Yu, Bin Yan, Xiyang Ji, Lin Li, Dong Wei, Zhaopan Li, Ping Chen

“…Therefore, we developed a prediction model for END based on these markers and evaluated its accuracy and clinical utility. …”
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Expression of p63 as predictive and prognostic factor in advanced non-small-cell lung cancer by Cvetković Gordana, Plavec Goran, Tatomirović Željka, Jović Milena, Lončarević Olivera, Trifunović Zoran, Vuković Jelena, Stojsavljević Marko, Milić Gordana

“…Methods. Expression of p63 in 85 NSCLC (stages III, and IV) was investigated by the use of immunohistochemistry. …”
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Predictive biomarkers and molecular subtypes in DLBCL: insights from PCD gene expression and machine learning by Tiantian He, Jie Geng, Chuandong Hou, Hongyi Li, Hong Zhang, Peng Zhao, Peifeng He, Xuechun Lu

“…Additionally, 12 machine learning algorithms were employed to construct predictive models for DLBCL, with performance evaluated using AUC and F-score metrics. …”
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Development of Predictive Nomograms for Clinical Use to Quantify the Risk of Amputation in Patients with Diabetic Foot Ulcer by Bocheng Peng, Rui Min, Yiqin Liao, Aixi Yu

“…Finally, the performance of the prediction model was evaluated and verified by receiver working characteristic (ROC) curve, corrected calibration curve, and clinical decision curve. …”
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Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects by Rashid Naseem, Bilal Khan, Arshad Ahmad, Ahmad Almogren, Saima Jabeen, Bashir Hayat, Muhammad Arif Shah

“…Tree family machine learning techniques are well-thought-out to be one of the finest and ordinarily used supervised learning methods. In this study, different tree family machine learning techniques are employed for software defect prediction using ten benchmark datasets. …”
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Predictive value of an early comprehensive assessment model for refractory mycoplasma pneumoniae pneumonia and internal validation by Xiangtao Wu, Weihong Lu, Weiqing Liu, Yishuai Ren, Shuangshuang Fan, Yali Xu, Ruigui Zhang, Xue Liu, Mengzhu Wang, Tuanjie Wang, Xingliang Zhang, Shaoru He, Shujun Li

“…There is currently a dearth of a comprehensive and efficient model for predicting and evaluating RMPP. Methods The development cohort consisted of patients with mycoplasma pneumoniae pneumonia (MPP) who underwent fiberoptic bronchoscopy between January 2019 and October 2021. …”
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Effects of oral maintenance chemotherapy and predictive value of circulating EBV DNA in metastatic nasopharyngeal carcinoma by Han Zhou, Tianzhu Lu, Qiaojuan Guo, Yan Chen, Mengwei Chen, Yansong Chen, Yingying Lin, Chuanben Chen, Liqin Ma, Yun Xu, Shaojun Lin, Jianji Pan

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Geophysical Log Responses and Predictive Modeling of Coal Quality in the Shanxi Formation, Northern Jiangsu, China by Xuejuan Song, Meng Wu, Nong Zhang, Yong Qin, Yang Yu, Yaqun Ren, Hao Ma

“…Traditional coal quality assessment methods rely exclusively on the laboratory testing of physical samples, which impedes detailed stratigraphic evaluation and limits the integration of intelligent precision mining technologies. …”
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Predictive Factors and Nomogram for 30-Day Mortality in Heatstroke Patients: A Retrospective Cohort Study by Anxin Li, Yuchen Zhang, Xiaoshi Zhang, Zixiao Duan, Yan Chen, Xiaoyan Jiang, Wuquan Deng

“…Methods: We conducted a retrospective cohort study, analyzing the clinical data of 203 HS patients between May 2016–September 2024. …”
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A predictive model for body water and fluid balance using 3D smartphone anthropometry by Austin J. Graybeal, Abby T. Compton, Sydney H. Swafford, Caleb F. Brandner, Molly F. Johnson, Maria G. Kaylor, Hunter Haynes, Jon Stavres

“…Smartphone-derived ICF was calculated as the difference between smartphone-predicted TBW and ECF. Fluid overload and imbalance were determined using ECF/TBW and ECF/ICF, respectively, and subsequently predicted from the retained variables using receiver operating characteristic curve analyses and logistic regression.ResultsEstimates from each of the newly-developed prediction models were not significantly different from the estimates produced using BIS (all p ≥ 0.70) and revealed acceptable agreement (TBW: R2 = 0.91, RMSE = 3.24 L; ECF: R2 = 0.94, RMSE = 1.10 L; ICF: R2 = 0.87, RMSE = 2.29 L) when evaluated in the testing sample (n = 66), although proportional bias was observed (p < 0.001). …”
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Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong by William Ka Kei Wu, Tong Liu, Ian Chi Kei Wong, Qingpeng Zhang, Jiandong Zhou, Sharen Lee, Keith Sai Kit Leung, Kamalan Jeevaratnam, Wing Tak Wong

“…Machine and deep learning approaches were used to improve overall survival prediction and were evaluated with fivefold cross validation method.Results A total of 273 678 patients (mean age: 65.4±12.7 years, male: 48.2%, median follow-up: 142 (IQR=106–142) months) were included, with 91 155 deaths occurring on follow-up (33.3%; annualized mortality rate: 3.4%/year; 2.7 million patient-years). …”
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The Predictive Value of Soluble Fms-Like Tyrosine Kinase-1 for Prognosis in COVID-19 Patients by Lai C, Wang Y, Shi F, Geng N, Liu Z, Pan W, Shi H, Ma Y, Liu B

“…Despite the availability of vaccines and treatments, effective biomarkers for predicting disease progression remain limited. This study aimed to investigate the prognostic value of soluble fms-like tyrosine kinase-1 (sFlt-1) in COVID-19 patients.Methods: A prospective cohort study was conducted involving 154 COVID-19 patients, with comprehensive clinical data and laboratory parameters analyzed to evaluate the effectiveness of sFlt-1 in determining disease severity and prognosis.Results: The results revealed that sFlt-1 levels correlated significantly with disease severity, showing higher levels in severe/critical cases compared to mild cases (P< 0.05). …”
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Nontraumatic headaches in the emergency department: identifying clinical associations and predictive warning signs of intracranial pathologies by Abdulaziz M. Alghamdi, Abdulkarim M. Alghamdi, Yousof Fahad Allarakia, Arwa S. Alghamdi, Abdulaziz S. Alrashid, Abdulwhab M. Alotaibi, Reem Addas, Ahmed I. Lary

“…BACKGROUND: Although headaches are common, only 3–21% are estimated to be secondary, with their causes ranging from non-alarming to life-threatening conditions. OBJECTIVES: Evaluate the associated factors and predictive warning signs of intracranial pathologies in patients presenting with nontraumatic headaches to the emergency document (ED). …”
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Metyrapone single administration, as a possible predictive tool of its dosage and timing in Cushing’s syndrome by Yasutaka Tsujimoto, Naoki Yamamoto, Hironori Bando, Masaaki Yamamoto, Yuka Ohmachi, Yuma Motomura, Yuka Oi-Yo, Yuriko Sasaki, Masaki Suzuki, Shin Urai, Michiko Takahashi, Genzo Iguchi, Wataru Ogawa, Hidenori Fukuoka

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Use of Saliva Analytes as a Predictive Model to Detect Diseases in the Pig: A Pilot Study by Eva Llamas-Amor, Alba Ortín-Bustillo, María José López-Martínez, Alberto Muñoz-Prieto, Edgar García Manzanilla, Julián Arense, Aida Miralles-Chorro, Pablo Fuentes, Silvia Martínez-Subiela, Antonio González-Bulnes, Elena Goyena, Andrea Martínez-Martínez, José Joaquín Cerón, Fernando Tecles

“…The aim of this research was to evaluate a panel of salivary analytes in three porcine diseases and establish predictive models to detect them. …”
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From helicobacter pylori to glucose metabolism: can DOB values serve as a predictive marker? by Yu Zhou, Jie Xu, Zhigang Huang, Daoxing He, Hui Duan, Yating Shi, Zhenjie Wang, Zhaoyi Chen

“…We investigated whether DOB could predict abnormal FBG in adults receiving routine health examinations.ObjectivesTo assess the association between H. pylori infection and metabolic abnormalities, and to evaluate the predictive utility of the DOB value for glycemic abnormalities.MethodsIn this retrospective study, 594 patients underwent both the 13C-UBT and metabolic parameter assessments. …”
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