A Prediction Model Optimization Critiques through Centroid Clustering by Reducing the Sample Size, Integrating Statistical and Machine Learning Techniques for Wheat Productivity by Muhammad Islam, Farrukh Shehzad

“…This study demonstrated strong evidences for deploying MLM for prediction of wheat productivity as an alternative of traditional statistical modeling.…”
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Predicting soil CO2 emissions and sinks due to soil management factors of Brachiaria decumbens pastures using Tier 2 IPCC Methodology by A. Silva-Parra, J. Mora-Delgado, L. M. Barrera-Rojas

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Uncovering the role of tumor cGAS expression in predicting response to PD-1/L1 inhibitors in non-small cell lung cancer by Yuichi Ozawa, Yasuhiro Koh, Ryota Shibaki, Yuhei Harutani, Hiroaki Akamatsu, Atsushi Hayata, Takeya Sugimoto, Yuka Kitamura, Junya Fukuoka, Masanori Nakanishi, Nobuyuki Yamamoto

“…Conclusion cGAS expression, but not STING, predicts poor PD-1/L1 inhibitor efficacy in NSCLC with high PD-L1, potentially due to a TGF-β-mediated immunosuppressive environment (UMIN000024414).…”
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A subtype of cancer‐associated fibroblast expressing syndecan‐2 (SDC2) predicts survival and immune checkpoint inhibitor response in gastric cancer by Ji‐Yong Sung, Jae‐Ho Cheong, Kihye Shin, Eui Tae Kim

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Increased Depressive and Anxiety Symptoms Predict Increased Severity of Functional Impairment After Five Years: A Nationally Representative Retrospective Cohort Study by Elisabeth C. DeMarco, Samantha Zocher, Blake Miyamoto, Leslie Hinyard, Divya S. Subramaniam

“…Using multivariate linear regression, poorer mental health status and increased comorbidity burden significantly predicted severity of functional limitation. While loneliness was associated with slightly worse function, increased social participation appeared to be a protective factor. …”
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Multivariable Diagnostic Prediction Model to Detect Hormone Secretion Profile From T2W MRI Radiomics with Artificial Neural Networks in Pituitary Adenomas by Begumhan BAYSAL, Mehmet Bilgin ESER, Mahmut Bilal DOGAN, Muhammet Arif KURSUN

“…A multivariable diagnostic prediction model was developed with artificial neural networks (ANN) for 7 outcomes. …”
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The Prediction Model of High-Frequency Ultrasound Combined with Artificial Intelligence-Assisted Scoring System Improved the Diagnosis of Sclerosing Adenosis and Early Breast Cancer by Ma B, Wu G, Zhu H, Liu Y, Hu W, Zhao J, Liu Y, Liu Q

“…Bingxin Ma,1 Gang Wu,1 Haohui Zhu,1 Yifei Liu,1 Wenjia Hu,1 Jing Zhao,1 Yinlong Liu,1 Qiuyu Liu2 1Department of Ultrasound, Henan Provincial People’s Hospital, Zhengzhou, 450000, People’s Republic of China; 2Department of Pathology, Henan Provincial People’s Hospital, Zhengzhou, 450000, People’s Republic of ChinaCorrespondence: Gang Wu, Email drwugang325@126.com; Haohui Zhu, Email zhh761126@163.comObjective: The study aimed to apply an artificial intelligence (AI)-assisted scoring system, and improve the diagnostic efficiency of Sclerosing adenosis and early breast cancer.Methods: This study retrospectively collected adenopathy patients (156 cases) and early breast cancer patients (150 cases) in Henan Provincial People’s Hospital from August 2020 to April 2023.Results: The area under the curve of the model constructed by clinical ultrasound features and combined AI features to predict and identify the two in the training group was 0.89 and 0.94, respectively. …”
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Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study by Yunzhen Ye, Yu Xiong, Qiongjie Zhou, Jiangnan Wu, Xiaotian Li, Xirong Xiao

“…In recent decades, extensive research has been devoted to the early prediction of GDM by various methods. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression. …”
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Maternal Uterine Artery Doppler and Serum Marker in the First Trimester as Predictive Markers for Small for Gestational Age Neonates and Preeclampsia: A Pilot Study by Je Yeon Lee, Kyung A Lee, So Yun Park, Soo Jung Kim, So-Yeon Shim, Young Ju Kim, Mi Hye Park

“…A lower serum beta-hCG level in the first trimester predicted the occurrence of SGA (adjusted odds ratio [AOR] = 0.53, <i>p</i> = 0.019), while the presence of the uterine artery notch in the first trimester predicted the development of PE (notch at least on one side: AOR = 8.65, <i>p</i> = 0.045 and notch on both sides: AOR = 8.91, <i>p</i> = 0.047). …”
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Evaluating NfL and NTproBNP as predictive biomarkers of intracranial injuries after mild traumatic brain injury in children presenting to emergency departments by Anne-Cécile Chiollaz, Virginie Pouillard, Michelle Seiler, Céline Habre, Fabrizio Romano, Céline Ritter Schenck, Fabian Spigariol, Christian Korff, Fabienne Maréchal, Verena Wyss, Lyssia Gruaz, Joan Montaner, Jean-Charles Sanchez, Sergio Manzano

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Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study by Yanfei Chen, Bing Wang, Yankai Shi, Wenhao Qi, Shihua Cao, Bingsheng Wang, Ruihan Xie, Jiani Yao, Xiajing Lou, Chaoqun Dong, Xiaohong Zhu, Danni He

“…Although existing AD risk prediction tools demonstrate high accuracy, their complexity and limited accessibility restrict practical application. …”
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Corrections to &#x201C;The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data&#x201D; by Ji Luo, Wuyang Zhuo, Siyan Liu, Bingfei Xu

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Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution model by Wenwen Cheng, Chen Yu, Xiaohui Liu

“…In terms of external data, the classification and prediction performance of various models are comprehensively evaluated. …”
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Nomogram for prognosis prediction in metastatic pancreatic cancer patients undergoing intra-arterial infusion chemotherapy: incorporating immune-inflammation scores and coagulation indicators by Yifan Yang, Shaoqi Zong, Yongqiang Hua

“…Conclusion The nomogram based on immune-inflammatory scores, coagulation indicators, and other clinicopathological factors can effectively predict the OS of patients with MPC.…”
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An Algorithm to Analyze Cost Heterogeneity using Counterfactual Scenarios in Endovascular versus Open Repair of Abdominal Aortic Aneurysm: Predicting Costs for Subsequent Patients by Christopher A. Jones, Peter W. Callas, Robert W. Everett, Richard A. Galbraith, Richie Spitsberg, Jeffrey J. Petrozzino, Michael J. DeSarno, Andrew C. Stanley

“…**Conclusions:** Certain risk factors at the individual patient level are predictive of UQC. Under such circumstances, it is our expectation that such algorithms may be used to select the most cost-efficient treatment.…”
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Residual HCV-RNA and Elevated Platelet-to-Lymphocyte Ratio Predict Poor Long-Term Outcomes in Patients with Chronic Hepatitis C After Treatment by Anna Wróblewska, Mateusz Gliwiński, Magda Rybicka, Małgorzata Cheba, Beata Lorenc, Piotr Trzonkowski, Krzysztof P. Bielawski, Katarzyna Sikorska

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Hereditary angioedema diagnosis evaluation score (HADES): A new clinical scoring system for predicting hereditary angioedema with C1 inhibitor deficiency by Ricardo Zwiener, MD, Rafael Zamora, MD, Carlos María Galmarini, MD, PhD, Laura Brion, PhD, Laura Arias, MD, Andrea Pino, MD, Paula Rozenfeld, PhD

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N-terminal pro-B-type natriuretic peptide as a predictive marker of the outcomes of fibrinolytic therapy in acute ST-elevation myocardial infarction by Amit Kumar, Shweta Tanwar, Rajesh Chetiwal

“…This study sought to ascertain the significance of NT-proBNP in predicting the outcome of thrombolysis in acute STEMI. …”
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Urinary I-FABP, L-FABP, TFF-3, and SAA Can Diagnose and Predict the Disease Course in Necrotizing Enterocolitis at the Early Stage of Disease by Stepan Coufal, Alena Kokesova, Helena Tlaskalova-Hogenova, Barbora Frybova, Jiri Snajdauf, Michal Rygl, Miloslav Kverka

“…Moreover, the combination of TFF-3 with I-FABP and SAA could predict pneumatosis intestinalis, and the combination of I-FABP, L-FABP, and SAA could predict gas in the portal vein or long-term hospitalization and low SAA predicts early full enteral feeding. …”
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Author Correction: Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea by Hyunji Sang, Jaeyu Park, Soeun Kim, Myeongcheol Lee, Hojae Lee, Sun-Ho Lee, Dong Keon Yon, Sang Youl Rhee

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